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Cai, Chun Hing. "Mining association rules with weighted items." Hong Kong : Chinese University of Hong Kong, 1998. http://www.cse.cuhk.edu.hk/%7Ekdd/assoc%5Frule/thesis%5Fchcai.pdf.
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Thesis (M. Phil.)--Chinese University of Hong Kong, 1998.<br>Description based on contents viewed Mar. 13, 2007; title from title screen. Includes bibliographical references (p. 99-103). Also available in print.
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Isik, Narin. "Fuzzy Spatial Data Cube Construction And Its Use In Association Rule Mining." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606056/index.pdf.
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The popularity of spatial databases increases since the amount of the spatial data that need to be handled has increased by the use of digital maps, images from satellites, video cameras, medical equipment, sensor networks, etc. Spatial data are difficult to examine and extract interesting knowledge<br>hence, applications that assist decision-making about spatial data like weather forecasting, traffic supervision, mobile communication, etc. have been introduced. In this thesis, more natural and precise knowledge from spatial data is generated by construction of fuzzy spatial data cube and extraction of fuzzy association rules from it in order to improve decision-making about spatial data. This involves an extensive research about spatial knowledge discovery and how fuzzy logic can be used to develop it. It is stated that incorporating fuzzy logic to spatial data cube construction necessitates a new method for aggregation of fuzzy spatial data. We illustrate how this method also enhances the meaning of fuzzy spatial generalization rules and fuzzy association rules with a case-study about weather pattern searching. This study contributes to spatial knowledge discovery by generating more understandable and interesting knowledge from spatial data by extending spatial generalization with fuzzy memberships, extending the spatial aggregation in spatial data cube construction by utilizing weighted measures, and generating fuzzy association rules from the constructed fuzzy spatial data cube.
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Unal, Calargun Seda. "Fuzzy Association Rule Mining From Spatio-temporal Data: An Analysis Of Meteorological Data In Turkey." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609308/index.pdf.
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Data mining is the extraction of interesting non-trivial, implicit, previously unknown and
potentially useful information or patterns from data in large databases. Association rule
mining is a data mining method that seeks to discover associations among transactions encoded
within a database. Data mining on spatio-temporal data takes into consideration the
dynamics of spatially extended systems for which large amounts of spatial data exist, given
that all real world spatial data exists in some temporal context. We need fuzzy sets in mining
association rules from spatio-temporal databases since fuzzy sets handle the numerical
data better by softening the sharp boundaries of data which models the uncertainty embedded
in the meaning of data. In this thesis, fuzzy association rule mining is performed
on spatio-temporal data using data cubes and Apriori algorithm. A methodology is developed
for fuzzy spatio-temporal data cube construction. Besides the performance criteria
interpretability, precision, utility, novelty, direct-to-the-point and visualization are defined
to be the metrics for the comparison of association rule mining techniques. Fuzzy association
rule mining using spatio-temporal data cubes and Apriori algorithm performed
within the scope of this thesis are compared using these metrics. Real meteorological data
(precipitation and temperature) for Turkey recorded between 1970 and 2007 are analyzed
using data cube and Apriori algorithm in order to generate the fuzzy association rules.
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Matthews, Stephen. "Learning lost temporal fuzzy association rules." Thesis, De Montfort University, 2012. http://hdl.handle.net/2086/8257.
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Fuzzy association rule mining discovers patterns in transactions, such as shopping baskets in a supermarket, or Web page accesses by a visitor to a Web site. Temporal patterns can be present in fuzzy association rules because the underlying process generating the data can be dynamic. However, existing solutions may not discover all interesting patterns because of a previously unrecognised problem that is revealed in this thesis. The contextual meaning of fuzzy association rules changes because of the dynamic feature of data. The static fuzzy representation and traditional search method are inadequate. The Genetic Iterative Temporal Fuzzy Association Rule Mining (GITFARM) framework solves the problem by utilising flexible fuzzy representations from a fuzzy rule-based system (FRBS). The combination of temporal, fuzzy and itemset space was simultaneously searched with a genetic algorithm (GA) to overcome the problem. The framework transforms the dataset to a graph for efficiently searching the dataset. A choice of model in fuzzy representation provides a trade-off in usage between an approximate and descriptive model. A method for verifying the solution to the hypothesised problem was presented. The proposed GA-based solution was compared with a traditional approach that uses an exhaustive search method. It was shown how the GA-based solution discovered rules that the traditional approach did not. This shows that simultaneously searching for rules and membership functions with a GA is a suitable solution for mining temporal fuzzy association rules. So, in practice, more knowledge can be discovered for making well-informed decisions that would otherwise be lost with a traditional approach.
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Sowan, Bilal I. "Enhancing Fuzzy Associative Rule Mining Approaches for Improving Prediction Accuracy. Integration of Fuzzy Clustering, Apriori and Multiple Support Approaches to Develop an Associative Classification Rule Base." Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5387.
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Building an accurate and reliable model for prediction for different application domains, is one of the most significant challenges in knowledge discovery and data mining. This thesis focuses on building and enhancing a generic predictive model for estimating a future value by extracting association rules (knowledge) from a quantitative database. This model is applied to several data sets obtained from different benchmark problems, and the results are evaluated through extensive experimental tests.
The thesis presents an incremental development process for the prediction model with three stages. Firstly, a Knowledge Discovery (KD) model is proposed by integrating Fuzzy C-Means (FCM) with Apriori approach to extract Fuzzy Association Rules (FARs) from a database for building a Knowledge Base (KB) to predict a future value. The KD model has been tested with two road-traffic data sets.
Secondly, the initial model has been further developed by including a diversification method in order to improve a reliable FARs to find out the best and representative rules. The resulting Diverse Fuzzy Rule Base (DFRB) maintains high quality and diverse FARs offering a more reliable and generic model. The model uses FCM to transform quantitative data into fuzzy ones, while a Multiple Support Apriori (MSapriori) algorithm is adapted to extract the FARs from fuzzy data. The correlation values for these FARs are calculated, and an efficient orientation for filtering FARs is performed as a post-processing method. The FARs diversity is maintained through the clustering of FARs, based on the concept of the sharing function technique used in multi-objectives optimization. The best and the most diverse FARs are obtained as the DFRB to utilise within the Fuzzy Inference System (FIS) for prediction.
The third stage of development proposes a hybrid prediction model called Fuzzy Associative Classification Rule Mining (FACRM) model. This model integrates the
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improved Gustafson-Kessel (G-K) algorithm, the proposed Fuzzy Associative Classification Rules (FACR) algorithm and the proposed diversification method. The improved G-K algorithm transforms quantitative data into fuzzy data, while the FACR generate significant rules (Fuzzy Classification Association Rules (FCARs)) by employing the improved multiple support threshold, associative classification and vertical scanning format approaches. These FCARs are then filtered by calculating the correlation value and the distance between them. The advantage of the proposed FACRM model is to build a generalized prediction model, able to deal with different application domains. The validation of the FACRM model is conducted using different benchmark data sets from the University of California, Irvine (UCI) of machine learning and KEEL (Knowledge Extraction based on Evolutionary Learning) repositories, and the results of the proposed FACRM are also compared with other existing prediction models. The experimental results show that the error rate and generalization performance of the proposed model is better in the majority of data sets with respect to the commonly used models.
A new method for feature selection entitled Weighting Feature Selection (WFS) is also proposed. The WFS method aims to improve the performance of FACRM model. The prediction performance is improved by minimizing the prediction error and reducing the number of generated rules. The prediction results of FACRM by employing WFS have been compared with that of FACRM and Stepwise Regression (SR) models for different data sets. The performance analysis and comparative study show that the proposed prediction model provides an effective approach that can be used within a decision support system.<br>Applied Science University (ASU) of Jordan
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Sowan, Bilal Ibrahim. "Enhancing fuzzy associative rule mining approaches for improving prediction accuracy : integration of fuzzy clustering, apriori and multiple support approaches to develop an associative classification rule base." Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5387.
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Building an accurate and reliable model for prediction for different application domains, is one of the most significant challenges in knowledge discovery and data mining. This thesis focuses on building and enhancing a generic predictive model for estimating a future value by extracting association rules (knowledge) from a quantitative database. This model is applied to several data sets obtained from different benchmark problems, and the results are evaluated through extensive experimental tests. The thesis presents an incremental development process for the prediction model with three stages. Firstly, a Knowledge Discovery (KD) model is proposed by integrating Fuzzy C-Means (FCM) with Apriori approach to extract Fuzzy Association Rules (FARs) from a database for building a Knowledge Base (KB) to predict a future value. The KD model has been tested with two road-traffic data sets. Secondly, the initial model has been further developed by including a diversification method in order to improve a reliable FARs to find out the best and representative rules. The resulting Diverse Fuzzy Rule Base (DFRB) maintains high quality and diverse FARs offering a more reliable and generic model. The model uses FCM to transform quantitative data into fuzzy ones, while a Multiple Support Apriori (MSapriori) algorithm is adapted to extract the FARs from fuzzy data. The correlation values for these FARs are calculated, and an efficient orientation for filtering FARs is performed as a post-processing method. The FARs diversity is maintained through the clustering of FARs, based on the concept of the sharing function technique used in multi-objectives optimization. The best and the most diverse FARs are obtained as the DFRB to utilise within the Fuzzy Inference System (FIS) for prediction. The third stage of development proposes a hybrid prediction model called Fuzzy Associative Classification Rule Mining (FACRM) model. This model integrates the ii improved Gustafson-Kessel (G-K) algorithm, the proposed Fuzzy Associative Classification Rules (FACR) algorithm and the proposed diversification method. The improved G-K algorithm transforms quantitative data into fuzzy data, while the FACR generate significant rules (Fuzzy Classification Association Rules (FCARs)) by employing the improved multiple support threshold, associative classification and vertical scanning format approaches. These FCARs are then filtered by calculating the correlation value and the distance between them. The advantage of the proposed FACRM model is to build a generalized prediction model, able to deal with different application domains. The validation of the FACRM model is conducted using different benchmark data sets from the University of California, Irvine (UCI) of machine learning and KEEL (Knowledge Extraction based on Evolutionary Learning) repositories, and the results of the proposed FACRM are also compared with other existing prediction models. The experimental results show that the error rate and generalization performance of the proposed model is better in the majority of data sets with respect to the commonly used models. A new method for feature selection entitled Weighting Feature Selection (WFS) is also proposed. The WFS method aims to improve the performance of FACRM model. The prediction performance is improved by minimizing the prediction error and reducing the number of generated rules. The prediction results of FACRM by employing WFS have been compared with that of FACRM and Stepwise Regression (SR) models for different data sets. The performance analysis and comparative study show that the proposed prediction model provides an effective approach that can be used within a decision support system.
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He, Yuanchen. "Fuzzy-Granular Based Data Mining for Effective Decision Support in Biomedical Applications." Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/cs_diss/12.
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Due to complexity of biomedical problems, adaptive and intelligent knowledge discovery and data mining systems are highly needed to help humans to understand the inherent mechanism of diseases. For biomedical classification problems, typically it is impossible to build a perfect classifier with 100% prediction accuracy. Hence a more realistic target is to build an effective Decision Support System (DSS). In this dissertation, a novel adaptive Fuzzy Association Rules (FARs) mining algorithm, named FARM-DS, is proposed to build such a DSS for binary classification problems in the biomedical domain. Empirical studies show that FARM-DS is competitive to state-of-the-art classifiers in terms of prediction accuracy. More importantly, FARs can provide strong decision support on disease diagnoses due to their easy interpretability. This dissertation also proposes a fuzzy-granular method to select informative and discriminative genes from huge microarray gene expression data. With fuzzy granulation, information loss in the process of gene selection is decreased. As a result, more informative genes for cancer classification are selected and more accurate classifiers can be modeled. Empirical studies show that the proposed method is more accurate than traditional algorithms for cancer classification. And hence we expect that genes being selected can be more helpful for further biological studies.
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Castro, Ricardo Ferreira Vieira de. "Análise de desempenho dos algoritmos Apriori e Fuzzy Apriori na extração de regras de associação aplicados a um Sistema de Detecção de Intrusos." Universidade do Estado do Rio de Janeiro, 2014. http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=8137.
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A extração de regras de associação (ARM - Association Rule Mining) de dados quantitativos tem sido pesquisa de grande interesse na área de mineração de dados. Com o crescente aumento das bases de dados, há um grande investimento na área de pesquisa na criação de algoritmos para melhorar o desempenho relacionado a quantidade de regras, sua relevância e a performance computacional. O algoritmo APRIORI, tradicionalmente usado na extração de regras de associação, foi criado originalmente para trabalhar com atributos categóricos. Geralmente, para usá-lo com atributos contínuos, ou quantitativos, é necessário transformar os atributos contínuos, discretizando-os e, portanto, criando categorias a partir dos intervalos discretos. Os métodos mais tradicionais de discretização produzem intervalos com fronteiras sharp, que podem subestimar ou superestimar elementos próximos dos limites das partições, e portanto levar a uma representação imprecisa de semântica. Uma maneira de tratar este problema é criar partições soft, com limites suavizados. Neste trabalho é utilizada uma partição fuzzy das variáveis contínuas, que baseia-se na teoria dos conjuntos fuzzy e transforma os atributos quantitativos em partições de termos linguísticos. Os algoritmos de mineração de regras de associação fuzzy (FARM - Fuzzy Association Rule Mining) trabalham com este princípio e, neste trabalho, o algoritmo FUZZYAPRIORI, que pertence a esta categoria, é utilizado. As regras extraídas são expressas em termos linguísticos, o que é mais natural e interpretável pelo raciocício humano. Os algoritmos APRIORI tradicional e FUZZYAPRIORI são comparado, através de classificadores associativos, baseados em regras extraídas por estes algoritmos. Estes classificadores foram aplicados em uma base de dados relativa a registros de conexões TCP/IP que destina-se à criação de um Sistema de Detecção de Intrusos.<br>The mining of association rules of quantitative data has been of great research interest in the area of data mining. With the increasing size of databases, there is a large investment in research in creating algorithms to improve performance related to the amount of rules, its relevance and computational performance. The APRIORI algorithm, traditionally used in the extraction of association rules, was originally created to work with categorical attributes. In order to use continuous attributes, it is necessary to transform the continuous attributes, through discretization, into categorical attributes, where each categorie corresponds to a discrete interval. The more traditional discretization methods produce intervals with sharp boundaries, which may underestimate or overestimate elements near the boundaries of the partitions, therefore inducing an inaccurate semantical representation. One way to address this problem is to create soft partitions with smoothed boundaries. In this work, a fuzzy partition of continuous variables, which is based on fuzzy set theory is used. The algorithms for mining fuzzy association rules (FARM - Fuzzy Association Rule Mining) work with this principle, and, in this work, the FUZZYAPRIORI algorithm is used. In this dissertation, we compare the traditional APRIORI and the FUZZYAPRIORI, through classification results of associative classifiers based on rules extracted by these algorithms. These classifiers were applied to a database of records relating to TCP / IP connections that aims to create an Intrusion Detection System.
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Miguel, Carlos Henrique 1983. "Método para identificação de perfis de produtos : estudo de caso automobilístico." [s.n.], 2015. http://repositorio.unicamp.br/jspui/handle/REPOSIP/265791.
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Orientador: Antônio Batocchio<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica<br>Made available in DSpace on 2018-08-27T18:08:13Z (GMT). No. of bitstreams: 1
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Previous issue date: 2015<br>Resumo: O objetivo do trabalho foi elaborar um método de identificação de perfis de produto que representa os grupos de características frequentes do produto nas compras efetuadas por seus clientes. Foi feita uma revisão de literatura sobre quais áreas de gestão são influenciadas pela identificação de perfis de produtos, dentre elas: Planejamento de Demanda, Cadeia de Valor, Cadeia de Suprimentos e Cadeia Logística. Mais especificamente, as subáreas mais afetadas são Entrega de Fornecedores Chaves em base no Just In Time e Sistema de Reposição Contínua. As tecnologias de identificação eletrônica de produtos produzidos em série (e. g. RF ID, código de barras e código QR) são formas de identificar cada venda de produto a ser utilizado pelo método. Dentre as técnicas aplicadas no método, os Conjuntos Fuzzy foram utilizados para categorizar as características quantitativas dos produtos, que passaram a ser a entrada para a Análise de Carrinho de Compras, possibilitando determinar cada perfil de produto através de mineração de dados por regras de associação. O Apriori foi um algoritmo apropriado para realizar a Análise de Carrinho de Compras, pois realiza mineração por regras de associação de conjunto de itens frequentes utilizando as regras de interesse: suporte, confiança e lift. O algoritmo está presente no pacote Arules do programa estatístico R. O pacote ArulesViz, que está presente no programa estatístico R, permite visualizar de forma gráfica os relacionamentos entre os itens do produto. O método foi aplicado a uma base de dados de pesquisa do setor automobilístico, retornando com sucesso os perfis de automóvel frequentes dentre as compras efetuadas pelos clientes<br>Abstract: This study aimed to prepare a product profile identification method representing the groups of common characteristics of the product in the purchases made by its customers. A literature review was made on which areas of management are influenced by the identification of product profiles, such as: Demand Planning, Value Chain, Supply Chain and Logistic Chain. Specifically the Keys Suppliers Delivery sub-areas based on Just in Time and Continuous Replacement System are the most affected. The electronic identification technologies of products produced in series (e.g. RF ID, barcode and QR code) are ways to identify each product sale to be used by the method. Among the techniques applied in the method, Fuzzy Sets were used to categorize the quantitative characteristics of the products, which are now the entrance to the Market Basket Analysis, allowing to find each product profile through data mining for association rules. The Apriori was an appropriate algorithm to perform Market Basket Analysis, as done by mining association rule set of frequent item sets using the rules of interest: support, confidence and lift. The algorithm is present in Arules package of statistical software R. The ArulesViz package, which is present in the R statistical software, displays graphically the relationships between the items of the product. The method was applied to a research database of the automotive sector successfully returning the frequent car profiles from purchases made by customers<br>Mestrado<br>Materiais e Processos de Fabricação<br>Mestre em Engenharia Mecânica
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Sousa, Rogério Pereira de. "Classificação linear de bovinos: criação de um modelo de decisão baseado na conformação de tipo “true type” como auxiliar a tomada de decisão na seleção de bovinos leiteiros." Universidade do Vale do Rio dos Sinos, 2016. http://www.repositorio.jesuita.org.br/handle/UNISINOS/5896.
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Previous issue date: 2016-08-29<br>IFTO - Instituto Federal de Educação, Ciência e Tecnologia do Tocantins<br>A seleção de bovinos leiteiros, através da utilização do sistema de classificação com características lineares de tipo, reflete no ganho de produção, na vida produtiva do animal, na padronização do rebanho, entre outros. Esta pesquisa operacional obteve suas informações através de pesquisas bibliográficas e análise de base de dados de classificações reais. O presente estudo, objetivou a geração de um modelo de classificação de bovinos leiteiros baseado em “true type”, para auxiliar os avaliadores no processamento e análise dos dados, ajudando na tomada de decisão quanto a seleção da vaca para aptidão leiteira, tornando os dados seguros para futuras consultas. Nesta pesquisa, aplica-se métodos computacionais à classificação de vacas leiteiras mediante a utilização mineração de dados e lógica fuzzy. Para tanto, realizou-se a análise em uma base de dado com 144 registros de animais classificados entre as categorias boa e excelente. A análise ocorreu com a utilização da ferramenta WEKA para extração de regras de associação com o algoritmo apriori, utilizando como métricas objetivas, suporte / confiança, e lift para determinar o grau de dependência da regra. Para criação do modelo de decisão com lógica fuzzy, fez-se uso da ferramenta R utilizando o pacote sets. Por meio dos resultados obtidos na mineração de regras, foi possível identificar regras relevantes ao modelo de classificação com confiança acima de 90%, indicando que as características avaliadas (antecedente) implicam em outras características (consequente), com uma confiança alta. Quanto aos resultados obtidos pelo modelo de decisão fuzzy, observa-se que, o modelo de classificação baseado em avaliações subjetivas fica suscetível a erros de classificação, sugerindo então o uso de resultados obtidos por regras de associação como forma de auxílio objetivo na classificação final da vaca para aptidão leiteira.<br>The selection of dairy cattle through the use of the rating system with linear type traits, reflected in increased production, the productive life of the animal, the standardization of the flock, among others. This operational research obtained their information through library research and basic analysis of actual ratings data. This study aimed to generate a dairy cattle classification model based on "true type" to assist the evaluators in the processing and analysis of data, helping in decision making and the selection of the cow to milk fitness, making the data safe for future reference. In this research, applies computational methods to the classification of dairy cows by using data mining and fuzzy logic. Therefore, we conducted the analysis on a data base with 144 animals records classified between good and excellent categories. Analysis is made with the use of WEKA tool for extraction of association rules with Apriori algorithm, using as objective metrics, support / confidence and lift to determine the degree of dependency rule. To create the decision model with fuzzy logic, it was made use of R using the tool sets package. Through the results obtained in the mining rules, it was possible to identify the relevant rules with confidence classification model above 90%, indicating that the characteristics assessed (antecedent) imply other characteristics (consequent), with a high confidence. As for the results obtained by the fuzzy decision model, it is observed that the classification model based on subjective assessments is susceptible to misclassification, suggesting then the use of results obtained by association rules as a way to aid goal in the final classification cow for dairy fitness
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Wong, Wai-kit. "Security in association rule mining." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/HKUTO/record/B39558903.
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Wong, Wai-kit, and 王偉傑. "Security in association rule mining." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B39558903.
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Ayres, Rodrigo Moura Juvenil. "Mineração de regras de associação generalizadas utilizando ontologias fuzzy e similaridade baseada em contexto." Universidade Federal de São Carlos, 2012. https://repositorio.ufscar.br/handle/ufscar/503.
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Previous issue date: 2012-08-08<br>Financiadora de Estudos e Projetos<br>The mining association rules are an important task in data mining. Traditional algorithms of mining association rules are based only on the database items, providing a very specific knowledge. This specificity may not be advantageous, because the users normally need more general, interesting and understandable knowledge. In this sense, there are approaches working in order to obtain association rules with items belonging to any level of a taxonomic structure. In the crisp contexts taxonomies are used in different steps of the mining process. When the objective is the generalization they are used, mainly, in the pre-processing or post-processing stages. On the other hand, in the fuzzy context, fuzzy taxonomies are used, mainly, in the pre-processing step, during the generating extended transactions. A great problem of these transactions is related to the huge amount of candidates and rules. Beyond that, the inclusion of ancestors ends up generating redundancy problems. Besides, it is possible to see that many works have directed efforts for the question of mining fuzzy rules, exploring linguistic terms, but few approaches have been proposed for explore new steps of mining process. In this sense, this paper proposes the Context FOntGAR algorithm, a new algorithm for mining generalized association rules under all levels of fuzzy ontologies composed by specialization/generalization degrees varying in the interval [0,1]. In order to obtain more semantic enrichment, the rules may be composed by similarity relations, which are represented at the fuzzy ontologies in different contexts. In this work the generalization is done during the post-processing step. Other relevant points of this paper are the specification of a new approach of generalization; including a new grouping rules treatment, and a new and efficient way for calculating both support and confidence of generalized rules.<br>Algoritmos tradicionais de associação se caracterizam por utilizar apenas itens contidos na base de dados, proporcionando um conhecimento muito específico. No entanto, essa especificidade nem sempre é vantajosa, pois normalmente os usuários finais necessitam de padrões mais gerais, e de fácil compreensão. Nesse sentido, existem abordagens que não se limitam somente aos itens da base, e trabalham com o objetivo de minerar regras (generalizadas) com itens presentes em qualquer nível de estruturas taxonômicas. Taxonomias podem ser utilizadas em diferentes etapas do processo de mineração. A literatura mostra que, em contextos crisp, essas estruturas são utilizadas tanto em etapa de pré-processamento, quanto em etapa de pós-processamento, e que em domínios fuzzy, a utilização ocorre somente na etapa de pré-processamento, durante a geração de transações estendidas. Além do viés de utilização de transações estendidas, que podem levar a geração de um volume de regras superior ao caso tradicional, é possível notar que, em domínios nebulosos, as pesquisas dão enfoque apenas à mineração de regras fuzzy, deixando de lado a exploração de diferentes graus de especialização/generalização em taxonomias. Nesse sentido, este trabalho propõem o algoritmo FOntGAR, um novo algoritmo para mineração de regras de associação generalizadas com itens presentes em qualquer nível de ontologias compostas por graus de especialização/generalização variando no intervalo [0,1] (ontologias de conceitos fuzzy), em etapa de pós-processamento. Objetivando obter maior enriquecimento semântico, as regras geradas pelo algoritmo também podem possuir relações de similaridade, de acordo com contextos pré-definidos. Outros pontos relevantes são a especificação de uma nova abordagem de generalização (incluindo um novo tratamento de agrupamento das regras), e um novo e eficiente método para calcular o suporte estendido das regras generalizadas durante a etapa mencionada.
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Palanisamy, Senthil Kumar. "Association rule based classification." Link to electronic thesis, 2006. http://www.wpi.edu/Pubs/ETD/Available/etd-050306-131517/.
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Thesis (M.S.)--Worcester Polytechnic Institute.<br>Keywords: Itemset Pruning, Association Rules, Adaptive Minimal Support, Associative Classification, Classification. Includes bibliographical references (p.70-74).
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Zhang, Ya Klein Cerry M. "Association rule mining in cooperative research." Diss., Columbia, Mo. : University of Missouri--Columbia, 2009. http://hdl.handle.net/10355/6540.
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The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Title from PDF of title page (University of Missouri--Columbia, viewed January 26, 2010). Thesis advisor: Dr. Cerry M. Klein. Includes bibliographical references.
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Icev, Aleksandar. "DARM distance-based association rule mining." Link to electronic thesis, 2003. http://www.wpi.edu/Pubs/ETD/Available/etd-0506103-132405.
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Vithal, Kadam Omkar. "Novel applications of Association Rule Mining- Data Stream Mining." AUT University, 2009. http://hdl.handle.net/10292/826.
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From the advent of association rule mining, it has become one of the most researched areas of data exploration schemes. In recent years, implementing association rule mining methods in extracting rules from a continuous flow of voluminous data, known as Data Stream has generated immense interest due to its emerging applications such as network-traffic analysis, sensor-network data analysis. For such typical kinds of application domains, the facility to process such enormous amount of stream data in a single pass is critical.
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Lin, Weiyang. "Association rule mining for collaborative recommender systems." Link to electronic version, 2000. http://www.wpi.edu/Pubs/ETD/Available/etd-0515100-145926.
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Escovar, Eduardo Luís Garcia. "Algoritmo SSDM para a mineração de dados semanticamente similares." Universidade Federal de São Carlos, 2004. https://repositorio.ufscar.br/handle/ufscar/495.
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Previous issue date: 2004-05-28<br>Financiadora de Estudos e Projetos<br>The SSDM algorithm, created to allow semantically similar data mining, is presented in this
work. Using fuzzy logic concepts, this algorithm analyzes the similarity grade between items,
considering it if it is greater than a user-defined parameter. When this occurs, fuzzy associations
between items are established, and are expressed in the association rules obtained. Therefore,
besides associations discovered by conventional algorithms, SSDM also discovers semantic
associations, showing them together with the other rules obtained. To do that, strategies are
defined to discover these associations and calculate the support and the confidence of the rules where they appear.<br>Neste trabalho é apresentado o algoritmo SSDM, criado para permitir a mineração de dados
semanticamente similares. Usando conceitos de lógica nebulosa, esse algoritmo analisa o grau de
similaridade entre os itens, e o considera caso ele seja maior do que um parâmetro definido pelo
usuário. Quando isso ocorre, são estabelecidas associações nebulosas entre os itens, que são
expressas nas regras de associação obtidas. Assim, além das associações descobertas por
algoritmos convencionais, o SSDM também descobre associações semânticas, e as exibe junto às demais regras obtidas. Para isso, são definidas estratégias para descobrir essas associações e para
calcular o suporte e a confiança das regras onde elas aparecem.
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Rantzau, Ralf. "Extended concepts for association rule discovery." [S.l. : s.n.], 1997. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB8937694.
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Ahmed, Shakil. "Strategies for partitioning data in association rule mining." Thesis, University of Liverpool, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.415661.
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Bogorny, Vania. "Enhancing spatial association rule mining in geographic databases." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2006. http://hdl.handle.net/10183/7841.
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A técnica de mineração de regras de associação surgiu com o objetivo de encontrar conhecimento novo, útil e previamente desconhecido em bancos de dados transacionais, e uma grande quantidade de algoritmos de mineração de regras de associação tem sido proposta na última década. O maior e mais bem conhecido problema destes algoritmos é a geração de grandes quantidades de conjuntos freqüentes e regras de associação. Em bancos de dados geográficos o problema de mineração de regras de associação espacial aumenta significativamente. Além da grande quantidade de regras e padrões gerados a maioria são associações do domínio geográfico, e são bem conhecidas, normalmente explicitamente representadas no esquema do banco de dados. A maioria dos algoritmos de mineração de regras de associação não garantem a eliminação de dependências geográficas conhecidas a priori. O resultado é que as mesmas associações representadas nos esquemas do banco de dados são extraídas pelos algoritmos de mineração de regras de associação e apresentadas ao usuário. O problema de mineração de regras de associação espacial pode ser dividido em três etapas principais: extração dos relacionamentos espaciais, geração dos conjuntos freqüentes e geração das regras de associação. A primeira etapa é a mais custosa tanto em tempo de processamento quanto pelo esforço requerido do usuário. A segunda e terceira etapas têm sido consideradas o maior problema na mineração de regras de associação em bancos de dados transacionais e tem sido abordadas como dois problemas diferentes: “frequent pattern mining” e “association rule mining”. Dependências geográficas bem conhecidas aparecem nas três etapas do processo. Tendo como objetivo a eliminação dessas dependências na mineração de regras de associação espacial essa tese apresenta um framework com três novos métodos para mineração de regras de associação utilizando restrições semânticas como conhecimento a priori. O primeiro método reduz os dados de entrada do algoritmo, e dependências geográficas são eliminadas parcialmente sem que haja perda de informação. O segundo método elimina combinações de pares de objetos geográficos com dependências durante a geração dos conjuntos freqüentes. O terceiro método é uma nova abordagem para gerar conjuntos freqüentes não redundantes e sem dependências, gerando conjuntos freqüentes máximos. Esse método reduz consideravelmente o número final de conjuntos freqüentes, e como conseqüência, reduz o número de regras de associação espacial.<br>The association rule mining technique emerged with the objective to find novel, useful, and previously unknown associations from transactional databases, and a large amount of association rule mining algorithms have been proposed in the last decade. Their main drawback, which is a well known problem, is the generation of large amounts of frequent patterns and association rules. In geographic databases the problem of mining spatial association rules increases significantly. Besides the large amount of generated patterns and rules, many patterns are well known geographic domain associations, normally explicitly represented in geographic database schemas. The majority of existing algorithms do not warrant the elimination of all well known geographic dependences. The result is that the same associations represented in geographic database schemas are extracted by spatial association rule mining algorithms and presented to the user. The problem of mining spatial association rules from geographic databases requires at least three main steps: compute spatial relationships, generate frequent patterns, and extract association rules. The first step is the most effort demanding and time consuming task in the rule mining process, but has received little attention in the literature. The second and third steps have been considered the main problem in transactional association rule mining and have been addressed as two different problems: frequent pattern mining and association rule mining. Well known geographic dependences which generate well known patterns may appear in the three main steps of the spatial association rule mining process. Aiming to eliminate well known dependences and generate more interesting patterns, this thesis presents a framework with three main methods for mining frequent geographic patterns using knowledge constraints. Semantic knowledge is used to avoid the generation of patterns that are previously known as non-interesting. The first method reduces the input problem, and all well known dependences that can be eliminated without loosing information are removed in data preprocessing. The second method eliminates combinations of pairs of geographic objects with dependences, during the frequent set generation. A third method presents a new approach to generate non-redundant frequent sets, the maximal generalized frequent sets without dependences. This method reduces the number of frequent patterns very significantly, and by consequence, the number of association rules.
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Shrestha, Anuj. "Association Rule Mining of Biological Field Data Sets." Thesis, North Dakota State University, 2017. https://hdl.handle.net/10365/28394.
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Association rule mining is an important data mining technique, yet, its use in association analysis of biological data sets has been limited. This mining technique was applied on two biological data sets, a genome and a damselfly data set. The raw data sets were pre-processed, and then association analysis was performed with various configurations. The pre-processing task involves minimizing the number of association attributes in genome data and creating the association attributes in damselfly data. The configurations include generation of single/maximal rules and handling single/multiple tier attributes. Both data sets have a binary class label and using association analysis, attributes of importance to each of these class labels are found. The results (rules) from association analysis are then visualized using graph networks by incorporating the association attributes like support and confidence, differential color schemes and features from the pre-processed data.<br>Bioinformatics Seed Grant Program NIH/UND<br>National Science Foundation (NSF) Grant IIA-1355466
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Chudán, David. "Association rule mining as a support for OLAP." Doctoral thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-201130.
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The aim of this work is to identify the possibilities of the complementary usage of two analytical methods of data analysis, OLAP analysis and data mining represented by GUHA association rule mining. The usage of these two methods in the context of proposed scenarios on one dataset presumes a synergistic effect, surpassing the knowledge acquired by these two methods independently. This is the main contribution of the work. Another contribution is the original use of GUHA association rules where the mining is performed on aggregated data. In their abilities, GUHA association rules outperform classic association rules referred to the literature. The experiments on real data demonstrate the finding of unusual trends in data that would be very difficult to acquire using standard methods of OLAP analysis, the time consuming manual browsing of an OLAP cube. On the other hand, the actual use of association rules loses a general overview of data. It is possible to declare that these two methods complement each other very well. The part of the solution is also usage of LMCL scripting language that automates selected parts of the data mining process. The proposed recommender system would shield the user from association rules, thereby enabling common analysts ignorant of the association rules to use their possibilities. The thesis combines quantitative and qualitative research. Quantitative research is represented by experiments on a real dataset, proposal of a recommender system and implementation of the selected parts of the association rules mining process by LISp-Miner Control Language. Qualitative research is represented by structured interviews with selected experts from the fields of data mining and business intelligence who confirm the meaningfulness of the proposed methods.
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Idoudi, Rihab. "Fouille de connaissances en diagnostic mammographique par ontologie et règles d'association." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2017. http://www.theses.fr/2017IMTA0005/document.
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Face à la complexité significative du domaine mammographique ainsi que l'évolution massive de ses données, le besoin de contextualiser les connaissances au sein d'une modélisation formelle et exhaustive devient de plus en plus impératif pour les experts. C'est dans ce cadre que s'inscrivent nos travaux de recherche qui s'intéressent à unifier différentes sources de connaissances liées au domaine au sein d'une modélisation ontologique cible. D'une part, plusieurs modélisations ontologiques mammographiques ont été proposées dans la littérature, où chaque ressource présente une perspective distincte du domaine d'intérêt. D'autre part, l'implémentation des systèmes d'acquisition des mammographies rend disponible un grand volume d'informations issues des faits passés, dont la réutilisation devient un enjeu majeur. Toutefois, ces fragments de connaissances, présentant de différentes évidences utiles à la compréhension de domaine, ne sont pas interopérables et nécessitent des méthodologies de gestion de connaissances afin de les unifier. C'est dans ce cadre que se situe notre travail de thèse qui s'intéresse à l'enrichissement d'une ontologie de domaine existante à travers l'extraction et la gestion de nouvelles connaissances (concepts et relations) provenant de deux courants scientifiques à savoir: des ressources ontologiques et des bases de données comportant des expériences passées. Notre approche présente un processus de couplage entre l'enrichissement conceptuel et l'enrichissement relationnel d'une ontologie mammographique existante. Le premier volet comporte trois étapes. La première étape dite de pré-alignement d'ontologies consiste à construire pour chaque ontologie en entrée une hiérarchie des clusters conceptuels flous. Le but étant de réduire l'étape d'alignement de deux ontologies entières en un alignement de deux groupements de concepts de tailles réduits. La deuxième étape consiste à aligner les deux structures des clusters relatives aux ontologies cible et source. Les alignements validés permettent d'enrichir l'ontologie de référence par de nouveaux concepts permettant d'augmenter le niveau de granularité de la base de connaissances. Le deuxième processus s'intéresse à l'enrichissement relationnel de l'ontologie mammographique cible par des relations déduites de la base de données de domaine. Cette dernière comporte des données textuelles des mammographies recueillies dans les services de radiologies. Ce volet comporte ces étapes : i) Le prétraitement des données textuelles ii) l'application de techniques relatives à la fouille de données (ou extraction de connaissances) afin d'extraire des expériences de nouvelles associations sous la forme de règles, iii) Le post-traitement des règles générées. Cette dernière consiste à filtrer et classer les règles afin de faciliter leur interprétation et validation par l'expert vi) L'enrichissement de l'ontologie par de nouvelles associations entre les concepts. Cette approche a été mise en 'uvre et validée sur des ontologies mammographiques réelles et des données des patients fournies par les hôpitaux Taher Sfar et Ben Arous<br>Facing the significant complexity of the mammography area and the massive changes in its data, the need to contextualize knowledge in a formal and comprehensive modeling is becoming increasingly urgent for experts. It is within this framework that our thesis work focuses on unifying different sources of knowledge related to the domain within a target ontological modeling. On the one hand, there is, nowadays, several mammographic ontological modeling, where each resource has a distinct perspective area of interest. On the other hand, the implementation of mammography acquisition systems makes available a large volume of information providing a decisive competitive knowledge. However, these fragments of knowledge are not interoperable and they require knowledge management methodologies for being comprehensive. In this context, we are interested on the enrichment of an existing domain ontology through the extraction and the management of new knowledge (concepts and relations) derived from two scientific currents: ontological resources and databases holding with past experiences. Our approach integrates two knowledge mining levels: The first module is the conceptual target mammographic ontology enrichment with new concepts extracting from source ontologies. This step includes three main stages: First, the stage of pre-alignment. The latter consists on building for each input ontology a hierarchy of fuzzy conceptual clusters. The goal is to reduce the alignment task from two full ontologies to two reduced conceptual clusters. The second stage consists on aligning the two hierarchical structures of both source and target ontologies. Thirdly, the validated alignments are used to enrich the reference ontology with new concepts in order to increase the granularity of the knowledge base. The second level of management is interested in the target mammographic ontology relational enrichment by novel relations deducted from domain database. The latter includes medical records of mammograms collected from radiology services. This section includes four main steps: i) the preprocessing of textual data ii) the application of techniques for data mining (or knowledge extraction) to extract new associations from past experience in the form of rules, iii) the post-processing of the generated rules. The latter is to filter and classify the rules in order to facilitate their interpretation and validation by expert, vi) The enrichment of the ontology by new associations between concepts. This approach has been implemented and validated on real mammographic ontologies and patient data provided by Taher Sfar and Ben Arous hospitals. The research work presented in this manuscript relates to knowledge using and merging from heterogeneous sources in order to improve the knowledge management process
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Baez, Monroy Vicente Oswaldo. "Neural networks as artificial memories for association rule mining." Thesis, University of York, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.437620.
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Fjällström, Peter. "A way to compare measures in association rule mining." Thesis, Umeå universitet, Statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-124903.
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Mahmood, Qazafi. "LC - an effective classification based association rule mining algorithm." Thesis, University of Huddersfield, 2014. http://eprints.hud.ac.uk/id/eprint/24274/.
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Classification using association rules is a research field in data mining that primarily uses association rule discovery techniques in classification benchmarks. It has been confirmed by many research studies in the literature that classification using association tends to generate more predictive classification systems than traditional classification data mining techniques like probabilistic, statistical and decision tree. In this thesis, we introduce a novel data mining algorithm based on classification using association called “Looking at the Class” (LC), which can be used in for mining a range of classification data sets. Unlike known algorithms in classification using the association approach such as Classification based on Association rule (CBA) system and Classification based on Predictive Association (CPAR) system, which merge disjoint items in the rule learning step without anticipating the class label similarity, the proposed algorithm merges only items with identical class labels. This saves too many unnecessary items combining during the rule learning step, and consequently results in large saving in computational time and memory. Furthermore, the LC algorithm uses a novel prediction procedure that employs multiple rules to make the prediction decision instead of a single rule. The proposed algorithm has been evaluated thoroughly on real world security data sets collected using an automated tool developed at Huddersfield University. The security application which we have considered in this thesis is about categorizing websites based on their features to legitimate or fake which is a typical binary classification problem. Also, experimental results on a number of UCI data sets have been conducted and the measures used for evaluation is the classification accuracy, memory usage, and others. The results show that LC algorithm outperformed traditional classification algorithms such as C4.5, PART and Naïve Bayes as well as known classification based association algorithms like CBA with respect to classification accuracy, memory usage, and execution time on most data sets we consider.
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Pray, Keith A. "Apriori Sets And Sequences: Mining Association Rules from Time Sequence Attributes." Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0506104-150831/.
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Thesis (M.S.) -- Worcester Polytechnic Institute.<br>Keywords: mining complex data; temporal association rules; computer system performance; stock market analysis; sleep disorder data. Includes bibliographical references (p. 79-85).
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Weitl, Harms Sherri K. "Temporal association rule methodologies for geo-spatial decision support /." free to MU campus, to others for purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3091989.
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Delpisheh, Elnaz, and University of Lethbridge Faculty of Arts and Science. "Two new approaches to evaluate association rules." Thesis, Lethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science, c2010, 2010. http://hdl.handle.net/10133/2530.
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Data mining aims to discover interesting and unknown patterns in large-volume data. Association rule mining is one of the major data mining tasks, which attempts to find inherent relationships among data items in an application domain, such as supermarket basket analysis. An essential post-process in an association rule mining task is the evaluation of association rules by measures for their interestingness. Different interestingness measures have been proposed and studied. Given an association rule mining task, measures are assessed against a set of user-specified properties. However, in practice, given the subjectivity and inconsistencies in property specifications, it is a non-trivial task to make appropriate measure selections. In this work, we propose two novel approaches to assess interestingness measures. Our first approach utilizes the analytic hierarchy process to capture quantitatively domain-dependent requirements on properties, which are later used in assessing measures. This approach not only eliminates any inconsistencies in an end user’s property specifications through consistency checking but also is invariant to the number of association rules. Our
second approach dynamically evaluates association rules according to a composite and
collective effect of multiple measures. It interactively snapshots the end user’s domain-
dependent requirements in evaluating association rules. In essence, our approach uses
neural networks along with back-propagation learning to capture the relative importance
of measures in evaluating association rules. Case studies and simulations have been conducted to show the effectiveness of our two approaches.<br>viii, 85 leaves : ill. ; 29 cm
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Hahsler, Michael, Kurt Hornik, and Thomas Reutterer. "Implications of probabilistic data modeling for rule mining." Institut für Statistik und Mathematik, WU Vienna University of Economics and Business, 2005. http://epub.wu.ac.at/764/1/document.pdf.
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Mining association rules is an important technique for discovering meaningful patterns in transaction databases. In the current literature, the properties of algorithms to mine associations are discussed in great detail. In this paper we investigate properties of transaction data sets from a probabilistic point of view. We present a simple probabilistic framework for transaction data and its implementation using the R statistical computing environment. The framework can be used to simulate transaction data when no associations are present. We use such data to explore the ability to filter noise of confidence and lift, two popular interest measures used for rule mining. Based on the framework we develop the measure hyperlift and we compare this new measure to lift using simulated data and a real-world grocery database.<br>Series: Research Report Series / Department of Statistics and Mathematics
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Marinica, Claudia. "Association Rule Interactive Post-processing using Rule Schemas and Ontologies - ARIPSO." Phd thesis, Université de Nantes, 2010. http://tel.archives-ouvertes.fr/tel-00912580.
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This thesis is concerned with the merging of two active research domains: Knowledge Discovery in Databases (KDD), more precisely the Association Rule Mining technique, and Knowledge Engineering (KE) with a main interest in knowledge representation languages developed around the Semantic Web. In Data Mining, the usefulness of association rule technique is strongly limited by the huge amount and the low quality of delivered rules. Experiments show that rules become almost impossible to use when their number exceeds 100. At the same time, nuggets are often represented by those rare (low support) unexpected association rules which are surprising to the user. Unfortunately, the lower the support is, the larger the volume of rules becomes. Thus, it is crucial to help the decision maker with an efficient technique to reduce the number of rules. To overcome this drawback, several methods have been proposed in the literature such as itemset concise representations, redundancy reduction, filtering, ranking and post-processing. Even though rule interestingness strongly depends on user knowledge and goals, most of the existing methods are generally based on data structure. For instance, if the user looks for unexpected rules, all the already known rules should be pruned. Or, if the user wants to focus on specific family of rules, only this subset of rules should be selected. In this context, we address two main issues: the integration of user knowledge in the discovery process and the interactivity with the user. The first issue requires defining an adapted formalism to express user knowledge with accuracy and flexibility such as ontologies in the Semantic Web. Second, the interactivity with the user allows a more iterative mining process where the user can successively test different hypotheses or preferences and focus on interesting rules. The main contributions of this work can be summarized as follows: (i) A model to represent user knowledge. First, we propose a new rule-like formalism, called Rule Schema, which allows the user to define his/her expectations regarding the rules through ontology concepts. Second, ontologies allow the user to express his/her domain knowledge by means of a high semantic model. Last, the user can choose among a set of Operators for interactive processing the one to be applied over each Rule Schema (i.e. pruning, conforming, unexpectedness, . . . ). (ii) A new post-processing approach, called ARIPSO (Association Rule Interactive Post-processing using rule Schemas and Ontologies), which helps the user to reduce the volume of the discovered rules and to improve their quality. It consists in an interactive process integrating user knowledge and expectations by means of the proposed model. At each step of ARIPSO, the interactive loop allows the user to change the provided information and to reiterate the post-processing phase which produces new results. (iii) The implementation in post-processing of the proposed approach. The developed tool is complete and operational, and it implements all the functionalities described in the approach. Also, it makes the connection between different elements like the set of rules and rule schemas stored in PMML/XML files, and the ontologies stored in OWL files and inferred by the Pellet reasoner. (iv) An adapted implementation without post-processing, called ARLIUS (Association Rule Local mining Interactive Using rule Schemas), consisting in an interactive local mining process guided by the user. It allows the user to focus on interesting rules without the necessity to extract all of them, and without minimum support limit. In this way, the user may explore the rule space incrementally, a small amount at each step, starting from his/her own expectations and discovering their related rules. (v) The experimental study analyzing the approach efficiency and the discovered rule quality. For this purpose, we used a real-life and large questionnaire database concerning customer satisfaction. For ARIPSO, the experimentation was carried out in complete cooperation with the domain expert. For different scenarios, from an input set of nearly 400 thousand association rules, ARIPSO filtered between 3 and 200 rules validated by the expert. Clearly, ARIPSO allows the user to significantly and efficiently reduce the input rule set. For ARLIUS, we experimented different scenarios over the same questionnaire database and we obtained reduced sets of rules (less than 100) with very low support.
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Rahman, Sardar Muhammad Monzurur, and mrahman99@yahoo com. "Data Mining Using Neural Networks." RMIT University. Electrical & Computer Engineering, 2006. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080813.094814.
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Data mining is about the search for relationships and global patterns in large databases that are increasing in size. Data mining is beneficial for anyone who has a huge amount of data, for example, customer and business data, transaction, marketing, financial, manufacturing and web data etc. The results of data mining are also referred to as knowledge in the form of rules, regularities and constraints. Rule mining is one of the popular data mining methods since rules provide concise statements of potentially important information that is easily understood by end users and also actionable patterns. At present rule mining has received a good deal of attention and enthusiasm from data mining researchers since rule mining is capable of solving many data mining problems such as classification, association, customer profiling, summarization, segmentation and many others. This thesis makes several contributions by proposing rule mining methods using genetic algorithms and neural networks. The thesis first proposes rule mining methods using a genetic algorithm. These methods are based on an integrated framework but capable of mining three major classes of rules. Moreover, the rule mining processes in these methods are controlled by tuning of two data mining measures such as support and confidence. The thesis shows how to build data mining predictive models using the resultant rules of the proposed methods. Another key contribution of the thesis is the proposal of rule mining methods using supervised neural networks. The thesis mathematically analyses the Widrow-Hoff learning algorithm of a single-layered neural network, which results in a foundation for rule mining algorithms using single-layered neural networks. Three rule mining algorithms using single-layered neural networks are proposed for the three major classes of rules on the basis of the proposed theorems. The thesis also looks at the problem of rule mining where user guidance is absent. The thesis proposes a guided rule mining system to overcome this problem. The thesis extends this work further by comparing the performance of the algorithm used in the proposed guided rule mining system with Apriori data mining algorithm. Finally, the thesis studies the Kohonen self-organization map as an unsupervised neural network for rule mining algorithms. Two approaches are adopted based on the way of self-organization maps applied in rule mining models. In the first approach, self-organization map is used for clustering, which provides class information to the rule mining process. In the second approach, automated rule mining takes the place of trained neurons as it grows in a hierarchical structure.
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Koukal, Bohuslav. "OLAP Recommender: Supporting Navigation in Data Cubes Using Association Rule Mining." Master's thesis, Vysoká škola ekonomická v Praze, 2017. http://www.nusl.cz/ntk/nusl-359132.
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Manual data exploration in data cubes and searching for potentially interesting and useful information starts to be time-consuming and ineffective from certain volume of the data. In my thesis, I designed, implemented and tested a system, automating the data cube exploration and offering potentially interesting views on OLAP data to the end user. The system is based on integration of two data analytics methods - OLAP analysis data visualisation and data mining, represented by GUHA association rules mining. Another contribution of my work is a research of possibilities how to solve differences between OLAP analysis and association rule mining. Implemented solutions of the differences include data discretization, dimensions commensurability, design of automatic data mining task algorithm based on the data structure and mapping definition between mined association rules and corresponding OLAP visualisation. The system was tested with real retail sales data and with EU structural funds data. The experiments proved that complementary usage of the association rule mining together with OLAP analysis identifies relationships in the data with higher success rate than the isolated use of both techniques.
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Jin, Weiqing. "Fuzzy classification based on fuzzy association rule mining." 2004. http://www.lib.ncsu.edu/theses/available/etd-12072004-130619/unrestricted/etd.pdf.
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Yu, Shao-Huei, and 游邵惠. "Using Self-Tuning Fuzzy Membership Functions for Optimal Association Rule Mining." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/6c323r.
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碩士<br>國立虎尾科技大學<br>資訊管理研究所<br>98<br>With the advancement in information technology, data mining is often used to generate the association rules by semantic or quantitative transaction data. But how to extract the value of hiding the importance information is a preoccupation. In recent years, the reasoning process of association rules can be removed the unimportant inference through defined fuzzy membership functions and threshold settings, but its subjective setting impact the correctness of the final numbers of association rules derived and mining results. And the past, most scholars only considered one-way fuzzy membership functions to affect the measurement criteria of mining association rules whether or not important, so that often leads to the exploration results with considerable bias.
Therefore, this thesis uses fuzzy data mining method based on genetic algorithm to excavate association rules from the numerical transactions. Using bi-directional approximate reasoning for data analysis, and establishing a fuzzy membership functions optimization model in situations of considering the user without the assistance of field experts and don’t rely on a good statistical background and data acquisition technology. Finally, the experimental results confirm that the proposed system architecture can infer the final major association rules data of hiding the importance information to excavate the real value of knowledge through self-tuning global optimal fuzzy membership functions. And its decision-making reference can help enterprises more effectively manage customer relationships.
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"Mining association rules with weighted items." 1998. http://library.cuhk.edu.hk/record=b5889513.
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by Cai, Chun Hing.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.<br>Includes bibliographical references (leaves 109-114).<br>Abstract also in Chinese.<br>Acknowledgments --- p.ii<br>Abstract --- p.iii<br>Chapter 1 --- Introduction --- p.1<br>Chapter 1.1 --- Main Categories in Data Mining --- p.1<br>Chapter 1.2 --- Motivation --- p.3<br>Chapter 1.3 --- Problem Definition --- p.4<br>Chapter 1.4 --- Experimental Setup --- p.5<br>Chapter 1.5 --- Outline of the thesis --- p.6<br>Chapter 2 --- Literature Survey on Data Mining --- p.8<br>Chapter 2.1 --- Statistical Approach --- p.8<br>Chapter 2.1.1 --- Statistical Modeling --- p.9<br>Chapter 2.1.2 --- Hypothesis testing --- p.10<br>Chapter 2.1.3 --- Robustness and Outliers --- p.11<br>Chapter 2.1.4 --- Sampling --- p.12<br>Chapter 2.1.5 --- Correlation --- p.15<br>Chapter 2.1.6 --- Quality Control --- p.16<br>Chapter 2.2 --- Artificial Intelligence Approach --- p.18<br>Chapter 2.2.1 --- Bayesian Network --- p.19<br>Chapter 2.2.2 --- Decision Tree Approach --- p.20<br>Chapter 2.2.3 --- Rough Set Approach --- p.21<br>Chapter 2.3 --- Database-oriented Approach --- p.23<br>Chapter 2.3.1 --- Characteristic and Classification Rules --- p.23<br>Chapter 2.3.2 --- Association Rules --- p.24<br>Chapter 3 --- Background --- p.27<br>Chapter 3.1 --- Iterative Procedure: Apriori Gen --- p.27<br>Chapter 3.1.1 --- Binary association rules --- p.27<br>Chapter 3.1.2 --- Apriori Gen --- p.29<br>Chapter 3.1.3 --- Closure Properties --- p.30<br>Chapter 3.2 --- Introduction of Weights --- p.31<br>Chapter 3.2.1 --- Motivation --- p.31<br>Chapter 3.3 --- Summary --- p.32<br>Chapter 4 --- Mining weighted binary association rules --- p.33<br>Chapter 4.1 --- Introduction of binary weighted association rules --- p.33<br>Chapter 4.2 --- Weighted Binary Association Rules --- p.34<br>Chapter 4.2.1 --- Introduction --- p.34<br>Chapter 4.2.2 --- Motivation behind weights and counts --- p.36<br>Chapter 4.2.3 --- K-support bounds --- p.37<br>Chapter 4.2.4 --- Algorithm for Mining Weighted Association Rules --- p.38<br>Chapter 4.3 --- Mining Normalized Weighted association rules --- p.43<br>Chapter 4.3.1 --- Another approach for normalized weighted case --- p.45<br>Chapter 4.3.2 --- Algorithm for Mining Normalized Weighted Association Rules --- p.46<br>Chapter 4.4 --- Performance Study --- p.49<br>Chapter 4.4.1 --- Performance Evaluation on the Synthetic Database --- p.49<br>Chapter 4.4.2 --- Performance Evaluation on the Real Database --- p.58<br>Chapter 4.5 --- Discussion --- p.65<br>Chapter 4.6 --- Summary --- p.66<br>Chapter 5 --- Mining Fuzzy Weighted Association Rules --- p.67<br>Chapter 5.1 --- Introduction to the Fuzzy Rules --- p.67<br>Chapter 5.2 --- Weighted Fuzzy Association Rules --- p.69<br>Chapter 5.2.1 --- Problem Definition --- p.69<br>Chapter 5.2.2 --- Introduction of Weights --- p.71<br>Chapter 5.2.3 --- K-bound --- p.73<br>Chapter 5.2.4 --- Algorithm for Mining Fuzzy Association Rules for Weighted Items --- p.74<br>Chapter 5.3 --- Performance Evaluation --- p.77<br>Chapter 5.3.1 --- Performance of the algorithm --- p.77<br>Chapter 5.3.2 --- Comparison of unweighted and weighted case --- p.79<br>Chapter 5.4 --- Note on the implementation details --- p.81<br>Chapter 5.5 --- Summary --- p.81<br>Chapter 6 --- Mining weighted association rules with sampling --- p.83<br>Chapter 6.1 --- Introduction --- p.83<br>Chapter 6.2 --- Sampling Procedures --- p.84<br>Chapter 6.2.1 --- Sampling technique --- p.84<br>Chapter 6.2.2 --- Algorithm for Mining Weighted Association Rules with Sampling --- p.86<br>Chapter 6.3 --- Performance Study --- p.88<br>Chapter 6.4 --- Discussion --- p.91<br>Chapter 6.5 --- Summary --- p.91<br>Chapter 7 --- Database Maintenance with Quality Control method --- p.92<br>Chapter 7.1 --- Introduction --- p.92<br>Chapter 7.1.1 --- Motivation of using the quality control method --- p.93<br>Chapter 7.2 --- Quality Control Method --- p.94<br>Chapter 7.2.1 --- Motivation of using Mil. Std. 105D --- p.95<br>Chapter 7.2.2 --- Military Standard 105D Procedure [12] --- p.95<br>Chapter 7.3 --- Mapping the Database Maintenance to the Quality Control --- p.96<br>Chapter 7.3.1 --- Algorithm for Database Maintenance --- p.98<br>Chapter 7.4 --- Performance Evaluation --- p.102<br>Chapter 7.5 --- Discussion --- p.104<br>Chapter 7.6 --- Summary --- p.105<br>Chapter 8 --- Conclusion and Future Work --- p.106<br>Chapter 8.1 --- Summary of the Thesis --- p.106<br>Chapter 8.2 --- Conclusions --- p.107<br>Chapter 8.3 --- Future Work --- p.108<br>Bibliography --- p.108<br>Appendix --- p.115<br>Chapter A --- Generating a random number --- p.115<br>Chapter B --- Hypergeometric distribution --- p.116<br>Chapter C --- Quality control tables --- p.117<br>Chapter D --- Rules extracted from the database --- p.120
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Tang, Yi-tsung, and 湯鎰聰. "Fuzzy Association Rules Mining Framework and Its Applications." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/84103759818136804499.
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碩士<br>南華大學<br>資訊管理學研究所<br>94<br>In this paper, two important issues of mining association rules are investigated. The first problem is the discovery of generalized fuzzy association rules in the transaction database. It''s an important data-mining task, because more general and qualitative knowledge can be uncovered for decision making. However, few algorithms have been proposed in the literature, moreover, the efficiency of these algorithms needs to be improved to handle real-world large datasets. The second problem is to discover association rules from the web usage data and the large itemsets identified in the transaction database. This kind of rules will be useful for marketing decision.
A cluster-based mining architecture is proposed to address the two problems. At first, an efficient fuzzy association rule miner, based on cluster-based fuzzy-sets tables, is presented to identify all the large fuzzy itemsets. This method requires less contrast to generate large itemsets. Next, a fuzzy rule discovery method is used to compute the confidence values for discovering the relationships between transaction database and browsing information database. An illustrated example is given to demonstrate the effectiveness of the proposed methods and experimental results show that CBFAR outperforms a known Apriori-based fuzzy association rules mining algorithm.
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Lin, Kuei Ying, and 林桂英. "Mining Fuzzy Multiple-level Association Rules and Fuzzy Sequential Patterns from Quantitative Data." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/83878981510147486751.
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碩士<br>義守大學<br>資訊工程學系<br>89<br>Many researchers in database and machine learning fields are primarily interested in data mining because it offers opportunities to discover useful information and important relevant patterns in large databases. Most previous studies have shown how binary valued transaction data may be handled. Transaction data in real-world applications usually consist of quantitative values, so designing a sophisticated data-mining algorithm able to deal with various types of data presents a challenge to workers in this research field.
This paper thus proposes two kinds of fuzzy mining algorithms, respectively for multiple-level association rules and sequential patterns, to extract knowledge implicit in transactions stored as quantitative values. The proposed fuzzy mining algorithms first transform quantitative values in transactions into linguistic terms, then filter them to find fuzzy association rules or sequential patterns by modifying the conventional mining algorithms. Each quantitative item uses only the linguistic term with the maximum cardinality or uses all possible linguistic terms in the mining processes. If only the linguistic terms with the maximum cardinalities are used, the number of fuzzy regions to be processed is the same as that of the original items. The algorithms therefore focus on the most important linguistic terms and reduce their time complexity. If all linguistic terms are used in the mining process, the derived set of rules or patterns is more complete, although computation is more complex. In addition, a web mining algorithm for fuzzy browsing patterns from the world wide web has also been proposed. The association rules and sequential patterns mined out thus exhibit important quantitative regularity in databases and can be used to provide some suggestions to appropriate supervisors.
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Chang, Kung Wei, and 張恭維. "The Web Mining Framework Combining Association Rules And Fuzzy Clusters." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/65551909349006885595.
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碩士<br>元智大學<br>資訊管理研究所<br>89<br>Lately, most studies have relied on statistic clustering techniques to analyze web user profile data in web mining. However, this approach can only sort each user session into a single cluster. That is, it ignores a user session may contain several browsing prefers. According to this insufficiency, fuzzy clustering techniques were proposed instead. But those methods only can use similarity score of session to calculate the similarity between pages. Therefore, if users browse the same web page by different paths, that causes wrong results.
This research proposes a framework which combines the fuzzy clustering and association rules. This approach filters out the noisy data, and employs association rules to calculate the confidence of the rule as the association between different URL addresses. Finally, an improved fuzzy clustering is adopted, which replaces the similarity score of session with the confidence between pages, to found out the user prefers effectively.
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Chen, Chun-Hao, and 陳俊豪. "Mining Membership Functions and Fuzzy Association Rules Using Genetic Algorithms." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/28575877979874074583.
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碩士<br>義守大學<br>資訊管理學系碩士班<br>92<br>Data mining is commonly used in attempts to induce association rules from transaction data. Most previous studies focused on mining from binary valued data. Transactions in real-world applications, however, usually consist of quantitative values. This thesis thus proposes three GA-based fuzzy data-mining methods for extracting both association rules and membership functions from quantitative transactions. All the three methods first use evolutional computation to find membership functions suitable for mining problems and then use the final best set of membership functions to mine fuzzy association rules. In the first method, the set of membership functions for all items are encoded into a string of real numbers. The fitness of each individual is evaluated using the number of large 1-itemsets and the suitability of the derived membership functions. The second method further extends the first method to improve the mining performance. It divides and conquers the derivation process of the membership functions. It maintains multiple populations of membership functions, with one population for one item’s membership functions. It can thus achieve a faster convergence than the first one. In the above two methods, the number of membership functions for each item is predefined. The third method considers dynamically adjusting the number of membership functions. Each individual is divided into two parts, control genes and parametric genes. Control genes are used to determine whether parametric genes are active or not, and parametric genes are used to represent the set of membership functions. The third method thus provides a more flexible way than the other two. At last, the experimental results show that the designed fitness functions can avoid the formation of bad kinds of membership functions and can provide important mining results to users.
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"Mining fuzzy association rules in large databases with quantitative attributes." 1997. http://library.cuhk.edu.hk/record=b5889060.
Full textAbstract:
by Kuok, Chan Man.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 1997.<br>Includes bibliographical references (leaves 74-77).<br>Abstract --- p.i<br>Acknowledgments --- p.iii<br>Chapter 1 --- Introduction --- p.1<br>Chapter 1.1 --- Data Mining --- p.2<br>Chapter 1.2 --- Association Rule Mining --- p.3<br>Chapter 2 --- Background --- p.6<br>Chapter 2.1 --- Framework of Association Rule Mining --- p.6<br>Chapter 2.1.1 --- Large Itemsets --- p.6<br>Chapter 2.1.2 --- Association Rules --- p.8<br>Chapter 2.2 --- Association Rule Algorithms For Binary Attributes --- p.11<br>Chapter 2.2.1 --- AIS --- p.12<br>Chapter 2.2.2 --- SETM --- p.13<br>Chapter 2.2.3 --- "Apriori, AprioriTid and AprioriHybrid" --- p.15<br>Chapter 2.2.4 --- PARTITION --- p.18<br>Chapter 2.3 --- Association Rule Algorithms For Numeric Attributes --- p.20<br>Chapter 2.3.1 --- Quantitative Association Rules --- p.20<br>Chapter 2.3.2 --- Optimized Association Rules --- p.23<br>Chapter 3 --- Problem Definition --- p.25<br>Chapter 3.1 --- Handling Quantitative Attributes --- p.25<br>Chapter 3.1.1 --- Discrete intervals --- p.26<br>Chapter 3.1.2 --- Overlapped intervals --- p.27<br>Chapter 3.1.3 --- Fuzzy sets --- p.28<br>Chapter 3.2 --- Fuzzy association rule --- p.31<br>Chapter 3.3 --- Significance factor --- p.32<br>Chapter 3.4 --- Certainty factor --- p.36<br>Chapter 3.4.1 --- Using significance --- p.37<br>Chapter 3.4.2 --- Using correlation --- p.38<br>Chapter 3.4.3 --- Significance vs. Correlation --- p.42<br>Chapter 4 --- Steps For Mining Fuzzy Association Rules --- p.43<br>Chapter 4.1 --- Candidate itemsets generation --- p.44<br>Chapter 4.1.1 --- Candidate 1-Itemsets --- p.45<br>Chapter 4.1.2 --- Candidate k-Itemsets (k > 1) --- p.47<br>Chapter 4.2 --- Large itemsets generation --- p.48<br>Chapter 4.3 --- Fuzzy association rules generation --- p.49<br>Chapter 5 --- Experimental Results --- p.51<br>Chapter 5.1 --- Experiment One --- p.51<br>Chapter 5.2 --- Experiment Two --- p.53<br>Chapter 5.3 --- Experiment Three --- p.54<br>Chapter 5.4 --- Experiment Four --- p.56<br>Chapter 5.5 --- Experiment Five --- p.58<br>Chapter 5.5.1 --- Number of Itemsets --- p.58<br>Chapter 5.5.2 --- Number of Rules --- p.60<br>Chapter 5.6 --- Experiment Six --- p.61<br>Chapter 5.6.1 --- Varying Significance Threshold --- p.62<br>Chapter 5.6.2 --- Varying Membership Threshold --- p.62<br>Chapter 5.6.3 --- Varying Confidence Threshold --- p.63<br>Chapter 6 --- Discussions --- p.65<br>Chapter 6.1 --- User guidance --- p.65<br>Chapter 6.2 --- Rule understanding --- p.67<br>Chapter 6.3 --- Number of rules --- p.68<br>Chapter 7 --- Conclusions and Future Works --- p.70<br>Bibliography --- p.74
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Liao, Hsiu-Shan, and 廖秀珊. "Mining Fuzzy Association Rules From Hierarchy Concept with Linguistic Variable." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/99515579566021420566.
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碩士<br>元智大學<br>資訊管理學系<br>97<br>Database is the key technology for current enterprises to store their data. It is not only used for storing information but also mining the unknown information that hidden in the database. There are various kinds of data mining technologies, but the most common one is the association rule. The association rule is mainly used to discuss the association of the purchased products in order to provide reference information for the decision-maker. According to the current researches of the association rule, minimum support and minimum confidence generally use pre-determined value as their basic requirement. However, how to identify that the value is sufficient is very tough. Besides, in fact there are tons of goods on the market, the more goods the more complicated the goods sets will be. Thus, the association rule is very difficult to discover. To this end, we raise the hierarchy concept of the transaction items to assist us to find out the association rule. In addition, the administrator of each hierarchy needs different messages. Therefore, use the association rule founded by hierarchy concept, different messages can be sent to different hierarchy.
Based on the FP-Growth, this study associates the framework of fuzzy theory and hierarchy concept to propose an approach- FMMA, Fuzzy Multiple-level data Mining Algorithm. This algorithm uses linguistic variables to solve the problem of finding out the sufficient value by using minimum support and minimum confidence. Furthermore, according to the level taxonomy concept, the sub-level only needs to consider the nods of its parent level’s frequent itemset, thus the required mining time and space can be reduced to a certain extent. The algorithm raised in this study is more humanity and more effective in enhancing the required executive time and information storage space. The mined association rule is presented by natural language which is closer to the human thought and is easier to understand.
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Komo, Zimpi Helen. "Mining fuzzy association rules on large numerical data : a data mining system for NAWN." 2003. http://hdl.handle.net/1993/19910.
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Hsu, Chu-Chun, and 許竹君. "Application of FFP-Growth to Data Mining by Fuzzy Association Rules." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/89551842993585884833.
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碩士<br>元智大學<br>資訊管理學系<br>97<br>Database is the common tool for data archive nowadays. It is valuable to obtain useful data and create new knowledge using data mining technologies to analyze and integrate the tremendous and ever-increasing data. The relational rule method which finds relations in database is the most widespread among the data mining technologies. However, it takes repetitive checking and scanning of the database using traditional relational rule algorithm, and consequently limited the data mining efficiency.
This research adopted the FP-Growth as basis and integrated fuzzy partition to propose the Fuzzy Frequent Pattern Growth (FFP-Growth) algorithm that integrated the FP-Growth double database compression and scanning and the fuzzy partition rule to determine the fuzzy group for each item. The feature of this algorithm is that the generate frequent patterns can be created after updating trade database, without re-scanning the original database and therefore improve the data mining efficiency.
The fuzzy association rules were investigated from the quantitative trade data in the third part of the research. In addition, due to the fast increase of internet applications, the useful web browse style was explored from web server browsing records. The explored knowledge can be used in marketing and management decision making, and create new business chances.
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Ko, Wen-Yuan, and 柯文元. "A Study on Mining Fuzzy Association Rules and Episode Rules for Intrusion Detection." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/51656525683160430785.
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碩士<br>大葉大學<br>資訊管理學系碩士班<br>93<br>At present, most of intrusion detection systems still existing excessive fault alerts problems, including high false positive and low detection rate. These erroneous alerts will let system managers take efforts to handle. Hence, how to enhance the detection ability is an important issue in computer security.
The thesis proposes an intrusion detection system based on the combination of association rules and frequency episode rules. Firstly, we use the fuzzy clustering technique to cluster network packets. Secondly, we also mine the possible rules using the fuzzy association rule technology from each cluster in order to discover single intrusion attack. Besides, on the purpose of discovering the intrusion phase and order, we further mine fuzzy frequency episode rules to discover the multiple serial relationship from each cluster. Finally, we construct the rules into normal and abnormal rule database, respectively. The contribution of the proposed schemes is to accelerate the detection rate of the single intrusion or multiple intrusions by using the fuzzy data mining technique. In this thesis, we also implement the proposed schemes to validate their feasibility.
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Lin, Han-Hsuan, and 林翰玄. "A Research of Applying Association Rules Mining to Samples of Fuzzy Sequence." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/27244720240902185329.
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碩士<br>元智大學<br>資訊管理學系<br>98<br>In the recent years, the popularity of advanced technology has made processing and gathering data easier, faster and handier. And the data base, through a long-time storage, accumulates a large amount of information and these pieces of info usually have certain level of connection. Therefore, it is now a vital issue, with the assistance of all the related info, to know how to figure out the most useful data and do the mining job efficiently.
This research shows that the combination of the two methods- Boolean Algorithm and Association Sequence Algorithm- can make Data Mining much more efficient and improve the defaults of traditional Sequence Mining- AprioriAll Algorithm- constantly scanning the interchangeable data base while producing Large Sequence Itemsets; in the mean time, the study also indicates the new combination can generate more reliable evidence when Fuzzy Theory is applied to do the Data Mining and fuzzy partition the purchase quantity.
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Martinus and Martinus. "Mining Spatial Colocation Patterns Using Data Field Model and Fuzzy Association Rules." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/71764064877230412811.
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碩士<br>亞洲大學<br>資訊工程學系碩士班<br>95<br>Scientists in many researches have been using computer technologies lately. GIS, GPS have been helping scientists in doing many kinds of researches. Geographical data as a result from GPS were available in electronic format. This type of data can be treated as a spatial data. And by using colocation pattern mining, we would discover associations between spatial features. The first thing we do was developed a data set generator. Data sets that are generated by data set generator then processed using the proposed approach.
The system we proposed was a two steps system. The first step was doing a segmentation to produce the transaction from the data set. The segmentation was using a fix threshold segmentation and the threshold was 3*sigma. Sigma in this study is our way to measure closeness of a point to its neighbors. Sigma is a distance value that will bring the entropy of the whole data set into it minimum, sigma was calculated using data field model. And the second one was doing a fuzzy association rule mining where we introduce the transaction into a fuzzy membership function. After fuzzfied the data set then we counted the fuzzy support values and fuzzy confidence values. The infrequent rules then pruned using an apriori-like algorithm. The result of the approach then come as these manners, feature a will be whether near or close or far from feature b.
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Kang, Sheng-Hsiang, and 康聖祥. "Fuzzy Data Mining with Multi-Level Association Rules and Support Tuning Mechanism." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/rtdffk.
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碩士<br>國立虎尾科技大學<br>資訊管理研究所<br>96<br>In the field of data mining, one application of the association rules is to analyze the relationship of the transaction data. We can find the valuable rules by using data mining. In addition, it can help a business to make decisions. Combining with Fuzzy sets and multi-level association rules, data mining will be more efficient. The apriori-like approach is a universal algorithm to find association rules. This algorithm scans database several times to mine the relations of products. However, it still takes much time and many storage spaces in the mining process. In practice, we often adjust support threshold several times to find the satisfied frequent pattern sets. When the minimum support threshold values are changed by using the apriori-like approach, we must rescan the database to mine new association rules. In such a way, it will take more time and storage spaces.
In this paper we propose a FMFP-tree (Fuzzy Mining Frequent Pattern tree) concept like FP-tree structure and a new algorithm FMQFP-Growth (Fuzzy Mining QFP-Growth). It improves the efficiency of the apriori-like approach when combining with Fuzzy sets and multi-level association rules. We also propose a Support Tuning Mechanism. When the minimum support threshold is changed, this algorithm just prunes in the P-tree or FMFP-tree and mines the association rules without rescanning database. Therefore, it can reduce mining time and storage spaces.