Mining Fuzzy Association Rules: An Overview

The main aim of this paper is to present a revision of the most relevant results about the use of Fuzzy Sets in Data Mining, specifically in relation with the discovery of Association Rules. Fuzzy Sets Theory has been shown to be a very useful tool in Data Mining in order to represent the so-called Association Rules in a natural and human-understandable way.

[1]  Keith C. C. Chan,et al.  Mining fuzzy association rules , 1997, CIKM '97.

[2]  Ulrich Güntzer,et al.  Algorithms for association rule mining — a general survey and comparison , 2000, SKDD.

[3]  Yasuhiko Morimoto,et al.  Mining Optimized Association Rules for Numeric Attributes , 1999, J. Comput. Syst. Sci..

[4]  Hisao Ishibuchi,et al.  Determination of rule weights of fuzzy association rules , 2001, 10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297).

[5]  Bernadette Bouchon-Meunier,et al.  Fuzzy Sets and Possibility Theory in Approximate and Plausible Reasoning , 1999 .

[6]  Guoqing Chen,et al.  Fuzzy association rules and the extended mining algorithms , 2002, Inf. Sci..

[7]  J.W.T. Lee,et al.  An ordinal framework for data mining of fuzzy rules , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).

[8]  Chi-Sheng Shih,et al.  Extracting classification knowledge of Internet documents with mining term associations: a semantic approach , 1998, SIGIR '98.

[9]  L. Zadeh A COMPUTATIONAL APPROACH TO FUZZY QUANTIFIERS IN NATURAL LANGUAGES , 1983 .

[10]  G. Pasi,et al.  A Fuzzy Linguistic Approach Generalizing Boolean Information Retrieval: a Model and its Evaluation , 1993 .

[11]  M. F. Porter,et al.  An algorithm for suffix stripping , 1997 .

[12]  Chengqi Zhang,et al.  A fuzzy logic based method to acquire user threshold of minimum-support for mining association rules , 2004, Inf. Sci..

[13]  Olga Pons,et al.  Weak and strong resemblance in fuzzy functional dependencies , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[14]  Patrick Bosc,et al.  Functional dependencies revisited under graduality and imprecision , 1997, 1997 Annual Meeting of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.97TH8297).

[15]  M. José Martín Bautista Modelos de computación flexible para la recuperación de información , 2000 .

[16]  Witold Pedrycz,et al.  Fuzzy set technology in knowledge discovery , 1998, Fuzzy Sets Syst..

[17]  Michalis Vazirgiannis A Classification and Relationship Extraction Scheme for Raltional Databases Based on Fuzzy Logic , 1998, PAKDD.

[18]  Philip S. Yu,et al.  An effective hash-based algorithm for mining association rules , 1995, SIGMOD '95.

[19]  Daniel Sánchez,et al.  Fuzzy cardinality based evaluation of quantified sentences , 2000, Int. J. Approx. Reason..

[20]  L. Zadeh,et al.  An Introduction to Fuzzy Logic Applications in Intelligent Systems , 1992 .

[21]  Keith C. C. Chan,et al.  An effective algorithm for discovering fuzzy rules in relational databases , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[22]  Christian Hidber,et al.  Association Rule Mining , 2017 .

[23]  Daniel Sánchez,et al.  Fuzzy association rules: general model and applications , 2003, IEEE Trans. Fuzzy Syst..

[24]  Heikki Mannila,et al.  Efficient Algorithms for Discovering Association Rules , 1994, KDD Workshop.

[25]  Rajeev Motwani,et al.  Beyond Market Baskets: Generalizing Association Rules to Dependence Rules , 1998, Data Mining and Knowledge Discovery.

[26]  Rajeev Motwani,et al.  Dynamic itemset counting and implication rules for market basket data , 1997, SIGMOD '97.

[27]  Tzung-Pei Hong,et al.  Mining association rules from quantitative data , 1999, Intell. Data Anal..

[28]  R. Yager Quantifier guided aggregation using OWA operators , 1996, Int. J. Intell. Syst..

[29]  Daniel Sánchez,et al.  Measuring the accuracy and interest of association rules: A new framework , 2002, Intell. Data Anal..

[30]  Wai-Ho Au,et al.  FARM: a data mining system for discovering fuzzy association rules , 1999, FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315).

[31]  Chad Creighton,et al.  Mining gene expression databases for association rules , 2003, Bioinform..

[32]  Ramakrishnan Srikant,et al.  Mining quantitative association rules in large relational tables , 1996, SIGMOD '96.

[33]  Philip S. Yu,et al.  Efficient mining of weighted association rules (WAR) , 2000, KDD '00.

[34]  Yi-Chung Hu,et al.  Elicitation of classification rules by fuzzy data mining , 2003 .

[35]  Martin Rajman,et al.  Text Mining: Natural Language techniques and Text Mining applications , 1998 .

[36]  Didier Dubois,et al.  A Discussion of Indices for the Evaluation of Fuzzy Associations in Relational Databases , 2003, IFSA.

[37]  Fernando Berzal Galiano Art: un método alternativo para la construcción de árboles de decisión , 2002 .

[38]  Weining Zhang,et al.  Mining fuzzy quantitative association rules , 1999, Proceedings 11th International Conference on Tools with Artificial Intelligence.

[39]  Daniel Sánchez,et al.  A probabilistic definition of a nonconvex fuzzy cardinality , 2002, Fuzzy Sets Syst..

[40]  Nicolás Marín,et al.  TBAR: An efficient method for association rule mining in relational databases , 2001, Data Knowl. Eng..

[41]  Jesús Chamorro-Martínez,et al.  Mining web documents to find additional query terms using fuzzy association rules , 2004, Fuzzy Sets Syst..

[42]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[43]  Donald H. Kraft,et al.  Performance measurement in a fuzzy retrieval environment , 1981, SIGIR 1981.

[44]  Daniel Sánchez,et al.  Mining Text Data: Special Features and Patterns , 2002, Pattern Detection and Discovery.

[45]  Jian Pei,et al.  Mining frequent patterns without candidate generation , 2000, SIGMOD 2000.

[46]  Maciej Wygralak Vaguely defined objects , 1995 .

[47]  Tzung-Pei Hong,et al.  Fuzzy data mining for interesting generalized association rules , 2003, Fuzzy Sets Syst..

[48]  Robert Meersman,et al.  On the Complexity of Mining Quantitative Association Rules , 1998, Data Mining and Knowledge Discovery.

[49]  Yi-Chung Hu,et al.  Discovering fuzzy association rules using fuzzy partition methods , 2003, Knowl. Based Syst..

[50]  Gerard Salton,et al.  Term-Weighting Approaches in Automatic Text Retrieval , 1988, Inf. Process. Manag..

[51]  Olga Pons,et al.  Soft computing: A new perspective for some data mining problems , 1997 .

[52]  Donald H. Kraft,et al.  Vocabulary mining for information retrieval: rough sets and fuzzy sets , 2001, Inf. Process. Manag..

[53]  Didier Dubois,et al.  Fuzzy rules in knowledge-based systems , 1992 .

[54]  Olga Pons,et al.  THE GENERALIZED SELECTION: AN ALTERNATIVE WAY FOR THE QUOTIENT OPERATIONS IN FUZZY RELATIONAL DATABASES , 1995 .

[55]  María Amparo Vila Miranda,et al.  A survey of methods to evaluate quantified sentences , 2000 .

[56]  Donald H. Kraft,et al.  Rules and fuzzy rules in text: concept, extraction and usage , 2003, Int. J. Approx. Reason..

[57]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[58]  Juan C. Cubero,et al.  A new definition of fuzzy functional dependency in fuzzy relational databases , 1994, Int. J. Intell. Syst..

[59]  Peter Willett,et al.  The Limitations of Term Co-Occurrence Data for Query Expansion in Document Retrieval Systems , 1991 .

[60]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[61]  Daming Shi,et al.  Mining fuzzy association rules with weighted items , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[62]  Daniel Sánchez,et al.  Acquisition of Fuzzy Association Rules from Medical Data , 2002 .

[63]  Daniel Sánchez,et al.  A New Framework to Assess Association Rules , 2001, IDA.

[64]  Man Hon Wong,et al.  Mining fuzzy association rules in databases , 1998, SGMD.

[65]  Etienne Kerre,et al.  Fuzzy Data Mining: Discovery of Fuzzy Generalized Association Rules+ , 2000 .

[66]  J. Kacprzyk,et al.  Fuzzy Logic with Linguistic Quantifiers: A Tool for Better Modeling of Human Evidence Aggregation Processes? , 1988 .

[67]  Arun N. Swami,et al.  Set-oriented mining for association rules in relational databases , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[68]  Yehuda Lindell,et al.  Text Mining at the Term Level , 1998, PKDD.

[69]  Gloria Bordogna,et al.  Fuzzy Approaches to Extend Boolean Information Retrieval , 1995 .

[70]  Evangelos Simoudis,et al.  Mining business databases , 1996, CACM.

[71]  Arbee L. P. Chen,et al.  The analysis of relationships in databases for rule derivation , 2004, Journal of Intelligent Information Systems.

[72]  Attila Gyenesei Mining Weighted Association Rules for Fuzzy Quantitative Items , 2000, PKDD.

[73]  Ramakrishnan Srikant,et al.  Mining generalized association rules , 1995, Future Gener. Comput. Syst..

[74]  Edward H. Shortliffe,et al.  A model of inexact reasoning in medicine , 1990 .

[75]  Renée J. Miller,et al.  Association rules over interval data , 1997, SIGMOD '97.

[76]  Eyke Hüllermeier,et al.  A Note on Quality Measures for Fuzzy Asscociation Rules , 2003, IFSA.

[77]  Kenji Satou,et al.  Extraction of knowledge on protein-protein interaction by association rule discovery , 2002, Bioinform..