Relevant bibliographies by topics / Fuzzy association mining

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  2. Dissertations / Theses
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Academic literature on the topic 'Fuzzy association mining'

Author: Grafiati
Published: 4 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "Fuzzy association mining"

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Lekha, A., C. V. Srikrishna, and Viji Vinod. "Fuzzy Association Rule Mining." Journal of Computer Science 11, no. 1 (2015): 71–74. http://dx.doi.org/10.3844/jcssp.2015.71.74.

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Sonia M, Delphin, John Robinson P, and Sebastian Rajasekaran A. "Mining Efficient Fuzzy Bio-Statistical Rules for Association of Sandalwood in Pachaimalai Hills." International Journal of Agricultural and Environmental Information Systems 6, no. 2 (2015): 40–76. http://dx.doi.org/10.4018/ijaeis.2015040104.

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The integration of association rules and correlation rules with fuzzy logic can produce more abstract and flexible patterns for many real life problems, since many quantitative features in real world, especially surveying the frequency of plant association in any region is fuzzy in nature. This paper presents a modification of a previously reported algorithm for mining fuzzy association and correlation rules, defines the concept of fuzzy partial and semi-partial correlation rule mining, and presents an original algorithm for mining fuzzy data based on correlation rule mining. It adds a regression model to the procedure for mining fuzzy correlation rules in order to predict one data instance from contributing more than others. It also utilizes statistical analysis for the data and the experimental results show a very high utility of fuzzy association rules and fuzzy correlation rule mining in modeling plant association problems. The newly proposed algorithm is utilized for seeking close associations and relationships between a group of plant species clustering around Sandalwood in Pachaimalai hills, Eastern Ghats, Tamilnadu.
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Nupur, Bhagoriya* Deepak Agrawal Zeba Qureshi. "TEMPORAL ASSOCIATION RULE MINING: A SURVEY IN FUZZY FRAMEWORK." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 4 (2017): 706–9. https://doi.org/10.5281/zenodo.569946.

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Temporal data mining generate temporal association rule that encapsulate transaction of item with time that’s recorded in temporal data base. Now these days recent research has focused to generate efficient fuzzy temporal association rule and transforming each quantitative value into fuzzy sets using the given membership functions. This paper presents a survey on temporal association rule and fuzzy logic. The Technical constraint of temporal data mining and fuzzy logic are identified and presented.
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Thomas, Binu, and G. Raju. "A Novel Web Classification Algorithm Using Fuzzy Weighted Association Rules." ISRN Artificial Intelligence 2013 (December 19, 2013): 1–10. http://dx.doi.org/10.1155/2013/316913.

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In associative classification method, the rules generated from association rule mining are converted into classification rules. The concept of association rule mining can be extended in web mining environment to find associations between web pages visited together by the internet users in their browsing sessions. The weighted fuzzy association rule mining techniques are capable of finding natural associations between items by considering the significance of their presence in a transaction. The significance of an item in a transaction is usually referred as the weight of an item in the transaction and finding associations between such weighted items is called fuzzy weighted association rule mining. In this paper, we are presenting a novel web classification algorithm using the principles of fuzzy association rule mining to classify the web pages into different web categories, depending on the manner in which they appear in user sessions. The results are finally represented in the form of classification rules and these rules are compared with the result generated using famous Boolean Apriori association rule mining algorithm.
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Han, Jianchao, and Mohsen Beheshti. "Discovering Both Positive and Negative Fuzzy Association Rules in Large Transaction Databases." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 3 (2006): 287–94. http://dx.doi.org/10.20965/jaciii.2006.p0287.

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Mining association rules is an important task of dara mining and knowledge discovery. Traditional association rules mining is built on transaction databases, which has some limitations. Two of these limitations are 1) each transaction merely contains binary items, meaning that an item either occurs in a transaction or not; 2) only positive association rules are discovered, while negative associations are ignored. Mining fuzzy association rules has been proposed to address the first limitation, while mining algorithms for negative association rules have been developed to resolve the second limitation. In this paper, we combine these two approaches to propose a novel approach for mining both positive and negative fuzzy association rules. The interestingness measure for both positive and negative fuzzy association rule is proposed, the algorithm for mining these rules is described, and an illustrative example is presented to demonstrate how the measure and the algorithm work.
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Subramanyam, R. B. V., and A. Goswami. "Mining fuzzy quantitative association rules." Expert Systems 23, no. 4 (2006): 212–25. http://dx.doi.org/10.1111/j.1468-0394.2006.00402.x.

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Lee, Wan-Jui, Jung-Yi Jiang, and Shie-Jue Lee. "Mining fuzzy periodic association rules." Data & Knowledge Engineering 65, no. 3 (2008): 442–62. http://dx.doi.org/10.1016/j.datak.2007.11.002.

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Marín, N., M. D. Ruiz, and D. Sánchez. "Fuzzy frameworks for mining data associations: fuzzy association rules and beyond." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 6, no. 2 (2016): 50–69. http://dx.doi.org/10.1002/widm.1176.

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Rajkamal Sarma. "Discovery of Fuzzy and Composite Fuzzy Association Rules in Meteorological Data." Journal of Information Systems Engineering and Management 10, no. 37s (2025): 677–97. https://doi.org/10.52783/jisem.v10i37s.6505.

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Fuzzy Association Rule Mining (FARM) extends traditional ARM by evaluating and pruning rules based on interestingness measures to identify relevant patterns for various applications. The focus of this paper is to explore the application of FARM techniques demonstrating its algorithmic implementation in a meteorological dataset. Three major algorithms known as fuzzy Apriori, FTDA (Fuzzy Transaction Data-Mining Algorithm) and CFARM Composite Fuzzy Association Rule Mining) are experimented and analyzed. The experiment uses a real meteorological dataset spanning twenty years consisting some important attributes of weather such as rainfall, temperature, relative humidity, wind speed and bright sunshine hours of the North Bank Plain Zone (NBPZ) of the Brahmaputra River in Assam, India. The collected dataset is pre-processed into a transaction dataset and converted into a fuzzy dataset using membership functions. The three FARM algorithms are subsequently employed to uncover associations among various attributes within the fuzzy meteorological dataset. This study analyzes experimental results from three algorithms, focusing on factors like rule generation, computation time, and memory consumption. While Fuzzy Apriori provides comprehensive rule generation, it comes at the cost of higher computation time and memory usage. FTDA and CFARM, on the other hand, offer more efficient and significant rule generation, making them more suitable for large-scale, complex data analysis. The findings of this paper can contribute to the development of resilient and efficient data mining frameworks, enhancing the decision-making process for stakeholders in the meteorological domain. Thus, the paper introduces a new method for analyzing meteorological data using Fuzzy Association Rule Mining (FARM) techniques.
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Intan, Rolly, Oviliani Yenty Yuliana, and Dwi Kristanto. "Bayesian Belief Network untuk Menghasilkan Fuzzy Association Rules." Jurnal Teknik Industri 12, no. 1 (2010): 55–60. http://dx.doi.org/10.9744/jti.12.1.55-60.

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Bayesian Belief Network (BBN), one of the data mining classification methods, is used in this research for mining and analyzing medical track record from a relational data table. In this paper, a mutual information concept is extended using fuzzy labels for determining the relation between two fuzzy nodes. The highest fuzzy information gain is used for mining fuzzy association rules in order to extend a BBN. Meaningful fuzzy labels can be defined for each domain data. For example, fuzzy labels of secondary disease and complication disease are defined for a disease classification. The implemented of the extended BBN in a application program gives a contribution for analyzing medical track record based on BBN graph and conditional probability tables.
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Dissertations / Theses on the topic "Fuzzy association mining"

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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 Miguel_CarlosHenrique_M.pdf: 3528187 bytes, checksum: 165344ab93862eb94649f13d1f4a8626 (MD5) 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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Submitted by Silvana Teresinha Dornelles Studzinski (sstudzinski) on 2016-11-01T15:54:48Z No. of bitstreams: 1 Rogério Pereira de Sousa_.pdf: 946780 bytes, checksum: ceb6c981273e15ecc58fe661bd02a34a (MD5)<br>Made available in DSpace on 2016-11-01T15:54:48Z (GMT). No. of bitstreams: 1 Rogério Pereira de Sousa_.pdf: 946780 bytes, checksum: ceb6c981273e15ecc58fe661bd02a34a (MD5) 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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Made available in DSpace on 2016-06-02T19:05:58Z (GMT). No. of bitstreams: 1 4486.pdf: 3511223 bytes, checksum: 3f8c09a3cb87230a2ac0f6706ea07944 (MD5) 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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Book chapters on the topic "Fuzzy association mining"

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Martín-Bautista, M. J., D. Sánchez, J. M. Serrano, and M. A. Vila. "Text Mining using Fuzzy Association Rules." In Fuzzy Logic and the Internet. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39988-9_9.

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Hüllermeier, Eyke. "Implication-Based Fuzzy Association Rules." In Principles of Data Mining and Knowledge Discovery. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44794-6_20.

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Fernandez-Bassso, Carlos, M. Dolores Ruiz, and Maria J. Martin-Bautista. "Fuzzy Association Rules Mining Using Spark." In Communications in Computer and Information Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91476-3_2.

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Kacprzyk, Janusz, and Slawomir Zadrożny. "Fuzzy Linguistic Summaries via Association Rules." In Data Mining and Computational Intelligence. Physica-Verlag HD, 2001. http://dx.doi.org/10.1007/978-3-7908-1825-3_5.

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Zhou, Zhongmei, Zhaohui Wu, Chunshan Wang, and Yi Feng. "Efficiently Mining Both Association and Correlation Rules." In Fuzzy Systems and Knowledge Discovery. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881599_42.

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Gyenesei, Attila, and Jukka Teuhola. "Interestingness Measures for Fuzzy Association Rules." In Principles of Data Mining and Knowledge Discovery. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44794-6_13.

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Pierrard, Régis, Jean-Philippe Poli, and Céline Hudelot. "A Fuzzy Close Algorithm for Mining Fuzzy Association Rules." In Communications in Computer and Information Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91476-3_8.

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Chen, Guoqing, Qiang Wei, and Etienne E. Kerre. "Fuzzy Data Mining: Discovery of Fuzzy Generalized Association Rules+." In Recent Issues on Fuzzy Databases. Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1845-1_3.

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Bin, Shen, Yao Min, and Yuan Bo. "Mining Weighted Generalized Fuzzy Association Rules with Fuzzy Taxonomies." In Computational Intelligence and Security. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11596448_104.

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Kaya, Mehmet, and Reda Alhajj. "Online Mining of Weighted Fuzzy Association Rules." In Computer and Information Sciences - ISCIS 2003. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39737-3_39.

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Conference papers on the topic "Fuzzy association mining"

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Chan, Keith C. C., and Wai-Ho Au. "Mining fuzzy association rules." In the sixth international conference. ACM Press, 1997. http://dx.doi.org/10.1145/266714.266898.

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Olson, David L., and Yanhong Li. "Mining Fuzzy Weighted Association Rules." In Proceedings of the 40th Annual Hawaii International Conference on System Sciences. IEEE, 2007. http://dx.doi.org/10.1109/hicss.2007.341.

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Zheng, Hui, Jing He, Guangyan Huang, and Yanchun Zhang. "Optimized fuzzy association rule mining for quantitative data." In 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2014. http://dx.doi.org/10.1109/fuzz-ieee.2014.6891735.

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Juvenil Ayres, Rodrigo Moura, and Marilde Terezinha Prado Santos. "FOntGAR algorithm: Mining generalized association rules using fuzzy ontologies." In 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2012. http://dx.doi.org/10.1109/fuzz-ieee.2012.6250804.

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Wang, Chien-Hua, Wei-Hsuan Lee, Chia-Hsuan Yeh, and Chin-Tzong Pang. "Fuzzy QMD algorithm for mining fuzzy association rules." In the 3rd International Conference. ACM Press, 2017. http://dx.doi.org/10.1145/3162957.3162986.

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TANG, HONGXIA, ZHENG PEI, LIANGZHONG YI, and ZUNWEI ZHANG. "MINING FUZZY ASSOCIATION RULES FROM DATABASE." In Proceedings of the 4th International ISKE Conference on Intelligent Systems and Knowledge Engineering. WORLD SCIENTIFIC, 2009. http://dx.doi.org/10.1142/9789814295062_0038.

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Sanchez, Oliver, Jose M. Moyano, Luciano Sanchez, and Jesus Alcala-Fadez. "Mining association rules in R using the package RKEEL." In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2017. http://dx.doi.org/10.1109/fuzz-ieee.2017.8015572.

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Burda, Michal, Viktor Pavliska, and Radek Valasek. "Parallel mining of fuzzy association rules on dense data sets." In 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2014. http://dx.doi.org/10.1109/fuzz-ieee.2014.6891780.

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Matthews, Stephen G., Mario A. Gongora, Adrian A. Hopgood, and Samad Ahmadi. "Temporal fuzzy association rule mining with 2-tuple linguistic representation." In 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2012. http://dx.doi.org/10.1109/fuzz-ieee.2012.6251173.

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Lopez, F. Javier, Armando Blanco, Fernando Garcia, and Antonio Marin. "Extracting Biological Knowledge by Fuzzy Association Rule Mining." In 2007 IEEE International Fuzzy Systems Conference. IEEE, 2007. http://dx.doi.org/10.1109/fuzzy.2007.4295431.

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