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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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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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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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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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Abur-rous, Maher Ragheb Mohammed. "Phishing website detection using intelligent data mining techniques : design and development of an intelligent association classification mining fuzzy based scheme for phishing website detection with an emphasis on e-banking." Thesis, University of Bradford, 2010. http://hdl.handle.net/10454/4873.
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Phishing techniques have not only grown in number, but also in sophistication. Phishers might have a lot of approaches and tactics to conduct a well-designed phishing attack. The targets of the phishing attacks, which are mainly on-line banking consumers and payment service providers, are facing substantial financial loss and lack of trust in Internet-based services. In order to overcome these, there is an urgent need to find solutions to combat phishing attacks. Detecting phishing website is a complex task which requires significant expert knowledge and experience. So far, various solutions have been proposed and developed to address these problems. Most of these approaches are not able to make a decision dynamically on whether the site is in fact phished, giving rise to a large number of false positives. This is mainly due to limitation of the previously proposed approaches, for example depending only on fixed black and white listing database, missing of human intelligence and experts, poor scalability and their timeliness. In this research we investigated and developed the application of an intelligent fuzzy-based classification system for e-banking phishing website detection. The main aim of the proposed system is to provide protection to users from phishers deception tricks, giving them the ability to detect the legitimacy of the websites. The proposed intelligent phishing detection system employed Fuzzy Logic (FL) model with association classification mining algorithms. The approach combined the capabilities of fuzzy reasoning in measuring imprecise and dynamic phishing features, with the capability to classify the phishing fuzzy rules. Different phishing experiments which cover all phishing attacks, motivations and deception behaviour techniques have been conducted to cover all phishing concerns. A layered fuzzy structure has been constructed for all gathered and extracted phishing website features and patterns. These have been divided into 6 criteria and distributed to 3 layers, based on their attack type. To reduce human knowledge intervention, Different classification and association algorithms have been implemented to generate fuzzy phishing rules automatically, to be integrated inside the fuzzy inference engine for the final phishing detection. Experimental results demonstrated that the ability of the learning approach to identify all relevant fuzzy rules from the training data set. A comparative study and analysis showed that the proposed learning approach has a higher degree of predictive and detective capability than existing models. Experiments also showed significance of some important phishing criteria like URL & Domain Identity, Security & Encryption to the final phishing detection rate. Finally, our proposed intelligent phishing website detection system was developed, tested and validated by incorporating the scheme as a web based plug-ins phishing toolbar. The results obtained are promising and showed that our intelligent fuzzy based classification detection system can provide an effective help for real-time phishing website detection. The toolbar successfully recognized and detected approximately 92% of the phishing websites selected from our test data set, avoiding many miss-classified websites and false phishing alarms.
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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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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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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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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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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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Pazhoumand-Dar, Hossein. "Unsupervised monitoring of an elderly person's activities of daily living using Kinect sensors and a power meter." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2017. https://ro.ecu.edu.au/theses/1971.
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The need for greater independence amongst the growing population of elderly people has made the concept of “ageing in place” an important area of research. Remote home monitoring strategies help the elderly deal with challenges involved in ageing in place and performing the activities of daily living (ADLs) independently. These monitoring approaches typically involve the use of several sensors, attached to the environment or person, in order to acquire data about the ADLs of the occupant being monitored.
Some key drawbacks associated with many of the ADL monitoring approaches proposed for the elderly living alone need to be addressed. These include the need to label a training dataset of activities, use wearable devices or equip the house with many sensors. These approaches are also unable to concurrently monitor physical ADLs to detect emergency situations, such as falls, and instrumental ADLs to detect deviations from the daily routine. These are all indicative of deteriorating health in the elderly.
To address these drawbacks, this research aimed to investigate the feasibility of unsupervised monitoring of both physical and instrumental ADLs of elderly people living alone via inexpensive minimally intrusive sensors. A hybrid framework was presented which combined two approaches for monitoring an elderly occupant’s physical and instrumental ADLs. Both approaches were trained based on unlabelled sensor data from the occupant’s normal behaviours. The data related to physical ADLs were captured from Kinect sensors and those related to instrumental ADLs were obtained using a combination of Kinect sensors and a power meter. Kinect sensors were employed in functional areas of the monitored environment to capture the occupant’s locations and 3D structures of their physical activities. The power meter measured the power consumption of home electrical appliances (HEAs) from the electricity panel.
A novel unsupervised fuzzy approach was presented to monitor physical ADLs based on depth maps obtained from Kinect sensors. Epochs of activities associated with each monitored location were automatically identified, and the occupant’s behaviour patterns during each epoch were represented through the combinations of fuzzy attributes. A novel membership function generation technique was presented to elicit membership functions for attributes by analysing the data distribution of attributes while excluding noise and outliers in the data. The occupant’s behaviour patterns during each epoch of activity were then classified into frequent and infrequent categories using a data mining technique. Fuzzy rules were learned to model frequent behaviour patterns. An alarm was raised when the occupant’s behaviour in new data was recognised as frequent with a longer than usual duration or infrequent with a duration exceeding a data-driven value.
Another novel unsupervised fuzzy approach to monitor instrumental ADLs took unlabelled training data from Kinect sensors and a power meter to model the key features of instrumental ADLs. Instrumental ADLs in the training dataset were identified based on associating the occupant’s locations with specific power signatures on the power line. A set of fuzzy rules was then developed to model the frequency and regularity of the instrumental activities tailored to the occupant. This set was subsequently used to monitor new data and to generate reports on deviations from normal behaviour patterns.
As a proof of concept, the proposed monitoring approaches were evaluated using a dataset collected from a real-life setting. An evaluation of the results verified the high accuracy of the proposed technique to identify the epochs of activities over alternative techniques. The approach adopted for monitoring physical ADLs was found to improve elderly monitoring. It generated fuzzy rules that could represent the person’s physical ADLs and exclude noise and outliers in the data more efficiently than alternative approaches. The performance of different membership function generation techniques was compared. The fuzzy rule set obtained from the output of the proposed technique could accurately classify more scenarios of normal and abnormal behaviours.
The approach for monitoring instrumental ADLs was also found to reliably distinguish power signatures generated automatically by self-regulated devices from those generated as a result of an elderly person’s instrumental ADLs. The evaluations also showed the effectiveness of the approach in correctly identifying elderly people’s interactions with specific HEAs and tracking simulated upward and downward deviations from normal behaviours. The fuzzy inference system in this approach was found to be robust in regards to errors when identifying instrumental ADLs as it could effectively classify normal and abnormal behaviour patterns despite errors in the list of the used HEAs.
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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.
Full textAbstract:
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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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.
Full textAbstract:
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.<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.
Full textAbstract:
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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Casanova, Anderson Araújo. "MINERAÇÃO DE DADOS: ALGORITMO DA CONFIANÇA INVERSA." Universidade Federal do Maranhão, 2005. http://tedebc.ufma.br:8080/jspui/handle/tede/373.
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Previous issue date: 2005-06-28<br>This work presents studies that culminated in the development of a data mining algorithm that
extracts knowledge in a more efficient way and allows for a better use of the collected
information. Decisions based on imprecise information and a lack of criteria can cause the
relatively few resources available to be poorly applied, burdening taxpayers and consequently
the state. This much-needed information which allows for the fairest and most efficient
application of available resources and which would facilitate the work of the users as well as
those who render the services should be based upon consideration of the great variety of
established criteria. The making of a decision should be based upon the evaluation of the most
varied types of data and be analyzed by specialists who can judge which are true needs, so
that the criteria for the search of knowledge may be defined. The Algorithm of Inverse
Confidence - ACI accomplishes data mining using the technique of association rules, and it
proposes a new measure that enlarges the dimension of extracted information through five
fixed rules. ACI also classifies and associates items, using the concept of the fuzzy logic,
through parameters established by the user. ACI was applied in the surgical center of
HUUFMA - Academical Hospital of the Federal University of Maranhão - envisioning the
extraction of knowledge (standards).<br>Este trabalho apresenta estudos que culminaram no desenvolvimento de um algoritmo de
mineração de dados que, faz extração de conhecimento e que possibilita um melhor
aproveitamento das informações coletadas. Decisões baseadas em informações imprecisas e
com falta de critérios podem fazer com que recursos, de qualquer tipo, sejam mal aplicados. A
informação necessária que tornem a aplicação dos recursos mais justa e eficiente, e que
facilitem o trabalho tanto dos usuários de um determinado serviço quanto aos que prestam o
serviço, devem ser baseadas considerando a grande variedade de critérios estabelecidos. A
tomada de decisão deve ser com base na avaliação dos mais variados tipa de dados e analisada
por especialistas que julguem quais as necessidades, para que os critérios de busca do
conhecimento sejam definidos. O Algoritmo da Confiança Inversa ACI realiza mineração de
dados utilizando a técnica de regras de associação e propõe uma nova medida que amplia a
dimensão das informações extraídas através de cinco regras fixas. O ACI também classifica e
associa itens similares, utilizando o conceito da lógica nebulosa (fuzzy logic), através de
parâmetro estabelecido pelo usuário. O ACI foi aplicado no centro cirúrgico do HUUFMA
Hospital Universitário da Universidade Federal do Maranhão visando à extração de
conhecimento (padrões).
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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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Chen, Yen-Hsu, and 陳彥旭. "Mining Fuzzy Association Patterns in Microarray Databases." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/34367862423895135205.
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碩士<br>國立成功大學<br>資訊工程學系碩博士班<br>93<br>In our research, we propose a novel method based on association rule and extends fuzzy theorem. We applied fuzzy theorem on analyzing microarray and call the method FAGE. The paper proposes a novel pattern and rule named REGER (Ripple Effective Gene Expression Rule). With fuzzy association, the technique transforms quantity into human linguistic term. The rules found by fuzzy association method are more readable. Some recent studies have shown that association rules could reveal the interactions and relations between genes which are not found by using traditional clustering method. Our method could find some extra rules which are not found in traditional association rules. For example, a rule likes "G1:L'G2:SH", which indicates G2 being slightly up-regulated whenever G1 is down-regulated. Our REGER rule may form "WSC4:L->SOK1:SH->HSP12:H". It shows that WSC4, SOK1 and HSP12 are active at the same time and its fuzzy items, "L->SH->H", are monotone. It may be caused by the genes are on the same pathway. Through empirical evaluation, our method finds extra rules than traditional one and we provide a novel rule for biologists for advance research and analyzing.
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"Mining association rules with weighted items." 1998. http://library.cuhk.edu.hk/record=b5889513.
Full textAbstract:
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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"Mining fuzzy association rules in large databases with quantitative attributes." 1997. http://library.cuhk.edu.hk/record=b5889060.
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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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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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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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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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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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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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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.
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Lin, Hsin-Hung, and 林欣弘. "Study on the Mining Fuzzy Association Rules on Questionnaire Contains Uncertain Data." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/dms697.
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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>In recent years, data mining has been widely used in various fields. Due to most of information may exist in the hidden knowledge and for promoting better understanding of the events. Data mining therefore is used to mining out the
characteristic style of the object data and explains the relationship between the existing behaviors or predicts future results. Today, although there are many rules of relevance information, but most are defined in the information available for the accurate and definitive research. However, this condition is incompatible with the current situation, because people often made various negligence or record defect. This will lead to information obtained is uncertain. Moreover, questionnaires often
adopt the numerical sequence answer for convenience, but limiting the thinking of the subjects. This thesis proposes a approach to mining association rules from the uncertain data by combining data mining, fuzzy set and other technology associated with mining methods by extending the study of Weng [18]. The important association rules will no longer are omitted due to the nature of the information by the proposed approach in this research. By computer simulation results showed that the proposed association rules have better and more relational grade in this study. Finally, the Land Rover (Discovery SUV) questionnaire data is used to compare the proposed method in this study by the use of Poly Analyst software. The results showed that the proposed algorithm is superior to the original research results obtained.
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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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Shih, Bo-kwei, and 施柏魁. "Mining fuzzy association rules from RFM data and transaction data for database marketing." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/08673818974241937707.
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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 RFM 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 RFM database. An illustrated example is given to demonstrate the effectiveness of the proposed methods .
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Guo, Zheng-huang, and 郭政煌. "A Study of Mining Fuzzy Association Rules from Transaction Databases by Using Clustering with Frequent Patterns." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/72635829465815368885.
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碩士<br>國立臺南大學<br>數位學習科技學系<br>95<br>The computer has become an essential tool, and a variety of records are stored in a database. For example, transaction information of a convenience store, transaction information of a department store, transaction information of an online shop, and the basic information of a census. In a huge amount of transaction data, data mining techniques to explore the hidden and useful information have always been a hot topic. It is very important to identify a suitable fuzzy membership function which can generate a lot of frequent item sets, and then we can find more useful rules for decision-making. Genetic Algorithms (GA) is able to locate accurate fuzzy membership function; however, the biggest deficiencies of GA are wasted training time and inconsiderate of the relation property of data. If we use the traditional method to cut off sections of a distribution averagely, and we can not make sure if the cut range is appropriate for the properties of the database. In this study, we make use of k-mean algorithm to obtain the centroid of fuzzy section which has been cut, and then we can explore the frequent item sets by adding the radius of fuzzy cutting which generate fuzzy membership functions. In terms of the relation among items, the study proposes a method which is named FBCFA (frequent-based clustering for fuzzy association). The difference between FBCFA and CFA is that k-mean algorithm is applied to FBCFA, and FBCFA adapts to the mining technology of k-mean algorithm and frequent item sets together. At last, we figure out the sets of fuzzy cutting sections and frequent item sets, and the experimental results show that the number of the frequent item set of FBCFA is more than the number of the frequent item set of CFA.
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Yang, Wan-Chi, and 楊婉祺. "Multi-level Fuzzy Mining with Multiple Minimum Supports Association Rules and Support Tuning Mechanism for E-learning Materials Recommendation." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/hzx626.
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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, association rules are used to analyze customers’ relations of the transaction database, and web usage mining is to visit the website of the user browsing logs. In the learning management system also contains a large number of learners browsing logs, so the use of association rules on e-Learning to explore the database to retrieve a learner''s behavior patterns. The association rules mining in the most common and widespread is the use of Apriori algorithms, some related researches applied about mining transaction database on multiple-level and quantitative association rules with multiple minimum supports. But their proposed algorithm based on Apriori algorithm that is an un-efficient on mining lower support threshold, long patterns and huge number of frequent patterns. So this thesis was to use P-tree and FP-tree like structure to propose a new MFMFP-tree and MFMQFP-Growth algorithms to mining multi-level fuzzy association rules from quantitative transactions with multiple minimum supports, and application on mining frequent patterns of learners’ behavior. Through learning management system to retrieve learning path on learner choose materials, to provide recommendation for next learning course. Finally, we also proposed support tuning mechanism under the new algorithms.
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Yuan, Xin. "Multi-approaches to achieve an advanced cognitive agent in a new type of parallel processing computer." Thesis, 2020. http://hdl.handle.net/2440/130741.
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In this work, we addressed the problem of developing an agent-based artificial general intelligence that can be implemented in compact and power-efficient electronic hardware. We proposed an approach intended to show the feasibility of using this conceptual hardware-based architecture to replicate simple cognitive behaviours. This research started with surveys on cognitive behaviours and their decision-making architectures and compared them with a production rule-based parallel processing computation architecture. In order to demonstrate their potential, a sample case study of the homing behaviour of honey bees was undertaken to demonstrate the possibility of reproducing cognitive behaviours using a production rule-based cognitive architecture. We developed rule-based agents for a mobile platform which, under experimental conditions, made decisions to retrace its path back to a target position by comparison with the reference images. The agent made consistent overall cognitive decisions using fuzzified elements and guided the system reliably to target positions. Then, the research shifted to finding cognitive data representations and constructing cognitive decision-making structures in that production rule-based system. We introduced a new symbolic way of describing the significant features in an image, which is to use a collection of fuzzy symbolic elements to describe the characteristics of the current environmental information. It filtered out any unnecessary details, yet retained sufficient information describing the frame to enable reliable comparisons between images for the purposes of navigation. Numerical data were converted into fuzzy symbolic representations of the surrounding environment. The modified Fuzzy Inference System includes the reasoning rules used to support the cognitive decision-making process. One of the main disadvantages of a rule-based approach is the effort spent on developing rules. In order to reduce the workload of developing rules manually for agents, a modified Association Rules Mining (ARM) method was introduced to discover effective rules for agents autonomously, based on training data sets. This novel rule development method has been demonstrated through a trainable autonomous parking system, which can develop rules for autonomous parking agents.<br>Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 2021