Relevant bibliographies by topics / Fuzzy Association Rule Mining

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Academic literature on the topic 'Fuzzy Association Rule Mining'

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Published: 4 June 2021
Last updated: 9 February 2022

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Journal articles on the topic "Fuzzy Association Rule Mining"

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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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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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Kalia, Harihar, Satchidananda Dehuri, and Ashish Ghosh. "A Survey on Fuzzy Association Rule Mining." International Journal of Data Warehousing and Mining 9, no. 1 (2013): 1–27. http://dx.doi.org/10.4018/jdwm.2013010101.

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Association rule mining is one of the fundamental tasks of data mining. The conventional association rule mining algorithms, using crisp set, are meant for handling Boolean data. However, in real life quantitative data are voluminous and need careful attention for discovering knowledge. Therefore, to extract association rules from quantitative data, the dataset at hand must be partitioned into intervals, and then converted into Boolean type. In the sequel, it may suffer with the problem of sharp boundary. Hence, fuzzy association rules are developed as a sharp knife to solve the aforesaid problem by handling quantitative data using fuzzy set. In this paper, the authors present an updated survey of fuzzy association rule mining procedures along with a discussion and relevant pointers for further research.
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Bai, Yi Ming, Xian Yao Meng, and Xin Jie Han. "Mining Fuzzy Association Rules in Quantitative Databases." Applied Mechanics and Materials 182-183 (June 2012): 2003–7. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.2003.

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In this paper, we introduce a novel technique for mining fuzzy association rules in quantitative databases. Unlike other data mining techniques who can only discover association rules in discrete values, the algorithm reveals the relationships among different quantitative values by traversing through the partition grids and produces the corresponding Fuzzy Association Rules. Fuzzy Association Rules employs linguistic terms to represent the revealed regularities and exceptions in quantitative databases. After the fuzzy rule base is built, we utilize the definition of Support Degree in data mining to reduce the rule number and save the useful rules. Throughout this paper, we will use a set of real data from a wine database to demonstrate the ideas and test the models.
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Pardeshi, Pramod, and Ujwala Patil. "Fuzzy Association Rule Mining- A Survey." International Journal of Scientific Research in Computer Science and Engineering 5, no. 6 (2017): 13–18. http://dx.doi.org/10.26438/ijsrcse/v5i6.1318.

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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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Abouzakhar, Nasser S., Huankai Chen, and Bruce Christianson. "An Enhanced Fuzzy ARM Approach for Intrusion Detection." International Journal of Digital Crime and Forensics 3, no. 2 (2011): 41–61. http://dx.doi.org/10.4018/jdcf.2011040104.

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The integration of fuzzy logic with data mining methods such as association rules has achieved interesting results in various digital forensics applications. As a data mining technique, the association rule mining (ARM) algorithm uses ranges to convert any quantitative features into categorical ones. Such features lead to the sudden boundary problem, which can be smoothed by incorporating fuzzy logic so as to develop interesting patterns for intrusion detection. This paper introduces a Fuzzy ARM-based intrusion detection model that is tested on the CAIDA 2007 backscatter network traffic dataset. Moreover, the authors present an improved algorithm named Matrix Fuzzy ARM algorithm for mining fuzzy association rules. The experiments and results that are presented in this paper demonstrate the effectiveness of integrating fuzzy logic with association rule mining in intrusion detection. The performance of the developed detection model is improved by using this integrated approach and improved algorithm.
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A, Anitha, and Freeda Jebamalar.S. "Predicting Dengue Using Fuzzy Association Rule Mining." International Journal of Computer Trends and Technology 67, no. 3 (2019): 72–74. http://dx.doi.org/10.14445/22312803/ijctt-v67i3p114.

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Watanabe, Toshihiko. "An Improvement of Fuzzy Association Rules Mining Algorithm Based on Redundancy of Rules." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 9 (2011): 1248–55. http://dx.doi.org/10.20965/jaciii.2011.p1248.

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In data mining approach, quantitative attributes should be appropriately dealt with as well as Boolean attributes. This paper presents an essential improvement for extracting fuzzy association rules from a database. The objective of this paper is to improve the computational time of mining and to prune extracted redundant rules simultaneously for an actual data mining application. In this paper, we define the redundancy of fuzzy association rules as a new concept for mining and prove essential theorems concerning the redundancy of fuzzy association rules. Then, we propose a basic algorithm based on the Apriori algorithm for rule extraction utilizing the redundancy of the extracted rules. The essential performance of the algorithmis evaluated through numerical experiments using benchmark data. Fromthe results, themethod is found to be promising in terms of computational time and redundant-rule pruning.
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Dissertations / Theses on the topic "Fuzzy Association Rule 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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Unal, Calargun Seda. "Fuzzy Association Rule Mining From Spatio-temporal Data: An Analysis Of Meteorological Data In Turkey." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609308/index.pdf.

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Data mining is the extraction of interesting non-trivial, implicit, previously unknown and potentially useful information or patterns from data in large databases. Association rule mining is a data mining method that seeks to discover associations among transactions encoded within a database. Data mining on spatio-temporal data takes into consideration the dynamics of spatially extended systems for which large amounts of spatial data exist, given that all real world spatial data exists in some temporal context. We need fuzzy sets in mining association rules from spatio-temporal databases since fuzzy sets handle the numerical data better by softening the sharp boundaries of data which models the uncertainty embedded in the meaning of data. In this thesis, fuzzy association rule mining is performed on spatio-temporal data using data cubes and Apriori algorithm. A methodology is developed for fuzzy spatio-temporal data cube construction. Besides the performance criteria interpretability, precision, utility, novelty, direct-to-the-point and visualization are defined to be the metrics for the comparison of association rule mining techniques. Fuzzy association rule mining using spatio-temporal data cubes and Apriori algorithm performed within the scope of this thesis are compared using these metrics. Real meteorological data (precipitation and temperature) for Turkey recorded between 1970 and 2007 are analyzed using data cube and Apriori algorithm in order to generate the fuzzy association rules.
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Matthews, Stephen. "Learning lost temporal fuzzy association rules." Thesis, De Montfort University, 2012. http://hdl.handle.net/2086/8257.

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Fuzzy association rule mining discovers patterns in transactions, such as shopping baskets in a supermarket, or Web page accesses by a visitor to a Web site. Temporal patterns can be present in fuzzy association rules because the underlying process generating the data can be dynamic. However, existing solutions may not discover all interesting patterns because of a previously unrecognised problem that is revealed in this thesis. The contextual meaning of fuzzy association rules changes because of the dynamic feature of data. The static fuzzy representation and traditional search method are inadequate. The Genetic Iterative Temporal Fuzzy Association Rule Mining (GITFARM) framework solves the problem by utilising flexible fuzzy representations from a fuzzy rule-based system (FRBS). The combination of temporal, fuzzy and itemset space was simultaneously searched with a genetic algorithm (GA) to overcome the problem. The framework transforms the dataset to a graph for efficiently searching the dataset. A choice of model in fuzzy representation provides a trade-off in usage between an approximate and descriptive model. A method for verifying the solution to the hypothesised problem was presented. The proposed GA-based solution was compared with a traditional approach that uses an exhaustive search method. It was shown how the GA-based solution discovered rules that the traditional approach did not. This shows that simultaneously searching for rules and membership functions with a GA is a suitable solution for mining temporal fuzzy association rules. So, in practice, more knowledge can be discovered for making well-informed decisions that would otherwise be lost with a traditional approach.
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Sowan, Bilal I. "Enhancing Fuzzy Associative Rule Mining Approaches for Improving Prediction Accuracy. Integration of Fuzzy Clustering, Apriori and Multiple Support Approaches to Develop an Associative Classification Rule Base." Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5387.

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Building an accurate and reliable model for prediction for different application domains, is one of the most significant challenges in knowledge discovery and data mining. This thesis focuses on building and enhancing a generic predictive model for estimating a future value by extracting association rules (knowledge) from a quantitative database. This model is applied to several data sets obtained from different benchmark problems, and the results are evaluated through extensive experimental tests. The thesis presents an incremental development process for the prediction model with three stages. Firstly, a Knowledge Discovery (KD) model is proposed by integrating Fuzzy C-Means (FCM) with Apriori approach to extract Fuzzy Association Rules (FARs) from a database for building a Knowledge Base (KB) to predict a future value. The KD model has been tested with two road-traffic data sets. Secondly, the initial model has been further developed by including a diversification method in order to improve a reliable FARs to find out the best and representative rules. The resulting Diverse Fuzzy Rule Base (DFRB) maintains high quality and diverse FARs offering a more reliable and generic model. The model uses FCM to transform quantitative data into fuzzy ones, while a Multiple Support Apriori (MSapriori) algorithm is adapted to extract the FARs from fuzzy data. The correlation values for these FARs are calculated, and an efficient orientation for filtering FARs is performed as a post-processing method. The FARs diversity is maintained through the clustering of FARs, based on the concept of the sharing function technique used in multi-objectives optimization. The best and the most diverse FARs are obtained as the DFRB to utilise within the Fuzzy Inference System (FIS) for prediction. The third stage of development proposes a hybrid prediction model called Fuzzy Associative Classification Rule Mining (FACRM) model. This model integrates the 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.

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Building an accurate and reliable model for prediction for different application domains, is one of the most significant challenges in knowledge discovery and data mining. This thesis focuses on building and enhancing a generic predictive model for estimating a future value by extracting association rules (knowledge) from a quantitative database. This model is applied to several data sets obtained from different benchmark problems, and the results are evaluated through extensive experimental tests. The thesis presents an incremental development process for the prediction model with three stages. Firstly, a Knowledge Discovery (KD) model is proposed by integrating Fuzzy C-Means (FCM) with Apriori approach to extract Fuzzy Association Rules (FARs) from a database for building a Knowledge Base (KB) to predict a future value. The KD model has been tested with two road-traffic data sets. Secondly, the initial model has been further developed by including a diversification method in order to improve a reliable FARs to find out the best and representative rules. The resulting Diverse Fuzzy Rule Base (DFRB) maintains high quality and diverse FARs offering a more reliable and generic model. The model uses FCM to transform quantitative data into fuzzy ones, while a Multiple Support Apriori (MSapriori) algorithm is adapted to extract the FARs from fuzzy data. The correlation values for these FARs are calculated, and an efficient orientation for filtering FARs is performed as a post-processing method. The FARs diversity is maintained through the clustering of FARs, based on the concept of the sharing function technique used in multi-objectives optimization. The best and the most diverse FARs are obtained as the DFRB to utilise within the Fuzzy Inference System (FIS) for prediction. The third stage of development proposes a hybrid prediction model called Fuzzy Associative Classification Rule Mining (FACRM) model. This model integrates the ii improved Gustafson-Kessel (G-K) algorithm, the proposed Fuzzy Associative Classification Rules (FACR) algorithm and the proposed diversification method. The improved G-K algorithm transforms quantitative data into fuzzy data, while the FACR generate significant rules (Fuzzy Classification Association Rules (FCARs)) by employing the improved multiple support threshold, associative classification and vertical scanning format approaches. These FCARs are then filtered by calculating the correlation value and the distance between them. The advantage of the proposed FACRM model is to build a generalized prediction model, able to deal with different application domains. The validation of the FACRM model is conducted using different benchmark data sets from the University of California, Irvine (UCI) of machine learning and KEEL (Knowledge Extraction based on Evolutionary Learning) repositories, and the results of the proposed FACRM are also compared with other existing prediction models. The experimental results show that the error rate and generalization performance of the proposed model is better in the majority of data sets with respect to the commonly used models. A new method for feature selection entitled Weighting Feature Selection (WFS) is also proposed. The WFS method aims to improve the performance of FACRM model. The prediction performance is improved by minimizing the prediction error and reducing the number of generated rules. The prediction results of FACRM by employing WFS have been compared with that of FACRM and Stepwise Regression (SR) models for different data sets. The performance analysis and comparative study show that the proposed prediction model provides an effective approach that can be used within a decision support system.
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He, Yuanchen. "Fuzzy-Granular Based Data Mining for Effective Decision Support in Biomedical Applications." Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/cs_diss/12.

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Due to complexity of biomedical problems, adaptive and intelligent knowledge discovery and data mining systems are highly needed to help humans to understand the inherent mechanism of diseases. For biomedical classification problems, typically it is impossible to build a perfect classifier with 100% prediction accuracy. Hence a more realistic target is to build an effective Decision Support System (DSS). In this dissertation, a novel adaptive Fuzzy Association Rules (FARs) mining algorithm, named FARM-DS, is proposed to build such a DSS for binary classification problems in the biomedical domain. Empirical studies show that FARM-DS is competitive to state-of-the-art classifiers in terms of prediction accuracy. More importantly, FARs can provide strong decision support on disease diagnoses due to their easy interpretability. This dissertation also proposes a fuzzy-granular method to select informative and discriminative genes from huge microarray gene expression data. With fuzzy granulation, information loss in the process of gene selection is decreased. As a result, more informative genes for cancer classification are selected and more accurate classifiers can be modeled. Empirical studies show that the proposed method is more accurate than traditional algorithms for cancer classification. And hence we expect that genes being selected can be more helpful for further biological studies.
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Castro, Ricardo Ferreira Vieira de. "Análise de desempenho dos algoritmos Apriori e Fuzzy Apriori na extração de regras de associação aplicados a um Sistema de Detecção de Intrusos." Universidade do Estado do Rio de Janeiro, 2014. http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=8137.

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A extração de regras de associação (ARM - Association Rule Mining) de dados quantitativos tem sido pesquisa de grande interesse na área de mineração de dados. Com o crescente aumento das bases de dados, há um grande investimento na área de pesquisa na criação de algoritmos para melhorar o desempenho relacionado a quantidade de regras, sua relevância e a performance computacional. O algoritmo APRIORI, tradicionalmente usado na extração de regras de associação, foi criado originalmente para trabalhar com atributos categóricos. Geralmente, para usá-lo com atributos contínuos, ou quantitativos, é necessário transformar os atributos contínuos, discretizando-os e, portanto, criando categorias a partir dos intervalos discretos. Os métodos mais tradicionais de discretização produzem intervalos com fronteiras sharp, que podem subestimar ou superestimar elementos próximos dos limites das partições, e portanto levar a uma representação imprecisa de semântica. Uma maneira de tratar este problema é criar partições soft, com limites suavizados. Neste trabalho é utilizada uma partição fuzzy das variáveis contínuas, que baseia-se na teoria dos conjuntos fuzzy e transforma os atributos quantitativos em partições de termos linguísticos. Os algoritmos de mineração de regras de associação fuzzy (FARM - Fuzzy Association Rule Mining) trabalham com este princípio e, neste trabalho, o algoritmo FUZZYAPRIORI, que pertence a esta categoria, é utilizado. As regras extraídas são expressas em termos linguísticos, o que é mais natural e interpretável pelo raciocício humano. Os algoritmos APRIORI tradicional e FUZZYAPRIORI são comparado, através de classificadores associativos, baseados em regras extraídas por estes algoritmos. Estes classificadores foram aplicados em uma base de dados relativa a registros de conexões TCP/IP que destina-se à criação de um Sistema de Detecção de Intrusos.<br>The mining of association rules of quantitative data has been of great research interest in the area of data mining. With the increasing size of databases, there is a large investment in research in creating algorithms to improve performance related to the amount of rules, its relevance and computational performance. The APRIORI algorithm, traditionally used in the extraction of association rules, was originally created to work with categorical attributes. In order to use continuous attributes, it is necessary to transform the continuous attributes, through discretization, into categorical attributes, where each categorie corresponds to a discrete interval. The more traditional discretization methods produce intervals with sharp boundaries, which may underestimate or overestimate elements near the boundaries of the partitions, therefore inducing an inaccurate semantical representation. One way to address this problem is to create soft partitions with smoothed boundaries. In this work, a fuzzy partition of continuous variables, which is based on fuzzy set theory is used. The algorithms for mining fuzzy association rules (FARM - Fuzzy Association Rule Mining) work with this principle, and, in this work, the FUZZYAPRIORI algorithm is used. In this dissertation, we compare the traditional APRIORI and the FUZZYAPRIORI, through classification results of associative classifiers based on rules extracted by these algorithms. These classifiers were applied to a database of records relating to TCP / IP connections that aims to create an Intrusion Detection System.
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Miguel, Carlos Henrique 1983. "Método para identificação de perfis de produtos : estudo de caso automobilístico." [s.n.], 2015. http://repositorio.unicamp.br/jspui/handle/REPOSIP/265791.

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Orientador: Antônio Batocchio<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica<br>Made available in DSpace on 2018-08-27T18:08:13Z (GMT). No. of bitstreams: 1 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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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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Books on the topic "Fuzzy Association Rule Mining"

1

Zhang, Chengqi, and Shichao Zhang, eds. Association Rule Mining. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46027-6.

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Gkoulalas-Divanis, Aris, and Vassilios S. Verykios. Association Rule Hiding for Data Mining. Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-6569-1.

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Gkoulalas-Divanis, Aris. Association rule hiding for data mining. Springer, 2010.

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Dass, Rajanish. Classification using association rules. Indian Institute of Management, 2008.

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Kazienko, Przemysław. Associations: Discovery, analysis and applications. Oficyna Wydawnicza Politechniki Wrocławskiej, 2008.

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Association Rule Mining: Models and Algorithms (Lecture Notes in Computer Science). Springer, 2002.

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Algorithms and Applications for Academic Search, Recommendation and Quantitative Association Rule Mining. River Publishers, 2018.

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Assoziationsregel-Algorithmen Fur Daten Mit Komplexer Struktur: Mit Anwendungen Im Web Mining (Informationstechnologie Und Okonomie). Peter Lang Publishing, 2003.

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Extensible Multi-Agent System for Heterogeneous Database Association Rule Mining and Unification. Storming Media, 1999.

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1978-, Koh Yun Sing, and Rountree Nathan 1974-, eds. Rare association rule mining and knowledge discovery: Technologies for infrequent and critical event detection. Information Science Reference, 2010.

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Book chapters on the topic "Fuzzy Association Rule Mining"

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Dockhorn, Alexander, Chris Saxton, and Rudolf Kruse. "Association Rule Mining for Unknown Video Games." In Fuzzy Approaches for Soft Computing and Approximate Reasoning: Theories and Applications. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54341-9_22.

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Isik, Narin, and Adnan Yazici. "Association Rule Mining using Fuzzy Spatial Data Cubes." In Geographic Uncertainty in Environmental Security. Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-6438-8_12.

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Pach, F. P., A. Gyenesei, P. Arval, and J. Abonyi. "Fuzzy Association Rule Mining for Model Structure Identification." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-36266-1_25.

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Wahl, Scott, and John Sheppard. "Association Rule Mining in Fuzzy Political Donor Communities." In Machine Learning and Data Mining in Pattern Recognition. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96133-0_18.

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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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Chen, Chun-Hao, Tzung-Pei Hong, and Yu Li. "Fuzzy Association Rule Mining with Type-2 Membership Functions." In Intelligent Information and Database Systems. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15705-4_13.

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Chen, Jing, Hui Zheng, Peng Li, Zhenjiang Zhang, Huawei Li, and Wei Liu. "Fuzzy Association Rule Mining Algorithm Based on Load Classifier." In Communications in Computer and Information Science. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2810-1_18.

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Conference papers on the topic "Fuzzy Association Rule Mining"

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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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Abhirami, K. "Web usage mining using fuzzy association rule." In 2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS). IEEE, 2016. http://dx.doi.org/10.1109/icetets.2016.7603022.

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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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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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Chen, Chun-Ling, Frank S. C. Tseng, and Tyne Liang. "Hierarchical Document Clustering Using Fuzzy Association Rule Mining." In 2008 3rd International Conference on Innovative Computing Information and Control. IEEE, 2008. http://dx.doi.org/10.1109/icicic.2008.305.

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Singh, Indu, and Rajni Jindal. "Detecting malicious transactions using Fuzzy Association rule mining." In 2016 Fifth International Conference on Eco-Friendly Computing and Communication Systems (ICECCS). IEEE, 2016. http://dx.doi.org/10.1109/eco-friendly.2016.7893246.

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Rahman, Tasnia, Mir Md Jahangir Kabir, and Monika Kabir. "Performance Evaluation of Fuzzy Association Rule Mining Algorithms." In 2019 4th International Conference on Electrical Information and Communication Technology (EICT). IEEE, 2019. http://dx.doi.org/10.1109/eict48899.2019.9068771.

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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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Ma, Tinghuai, Jiazhao Leng, and Keyi Li. "Full-scale privacy preserving for association rule mining." In 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2010. http://dx.doi.org/10.1109/fskd.2010.5569380.

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Liu, Jian, and Yan-Qing Wang. "Web log data mining based on association rule." In 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2011). IEEE, 2011. http://dx.doi.org/10.1109/fskd.2011.6019878.

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