Fuzzy association rules: general model and applications

The theory of fuzzy sets has been recognized as a suitable tool to model several kinds of patterns that can hold in data. In this paper, we are concerned with the development of a general model to discover association rules among items in a (crisp) set of fuzzy transactions. This general model can be particularized in several ways; each particular instance corresponds to a certain kind of pattern and/or repository of data. We describe some applications of this scheme, paying special attention to the discovery of fuzzy association rules in relational databases.

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

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

[3]  Gregory Piatetsky-Shapiro,et al.  Knowledge Discovery in Databases: An Overview , 1992, AI Mag..

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

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

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

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

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

[9]  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).

[10]  Keith C. C. Chan,et al.  Mining changes in association rules: a fuzzy approach , 2005, Fuzzy Sets Syst..

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

[12]  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).

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

[14]  Lotfi A. Zadeh,et al.  Fuzzy logic = computing with words , 1996, IEEE Trans. Fuzzy Syst..

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

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

[17]  AgrawalRakesh,et al.  Mining quantitative association rules in large relational tables , 1996 .

[18]  S YuPhilip,et al.  An effective hash-based algorithm for mining association rules , 1995 .

[19]  R. Nelsen.  An Introduction to Copulas , 1998 .

[20]  Walter A. Kosters,et al.  Interesting Fuzzy Association Rules in Quantitative Databases , 2001, PKDD.

[21]  Jan M. Zytkow,et al.  From Contingency Tables to Various Forms of Knowledge in Databases , 1996, Advances in Knowledge Discovery and Data Mining.

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

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

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

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

[26]  Heikki Mannila,et al.  Discovery of Frequent Episodes in Event Sequences , 1997, Data Mining and Knowledge Discovery.

[27]  Giangiacomo Gerla,et al.  Nonstandard Entropy and Energy Measures of a Fuzzy Set , 1984 .

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

[29]  Chris Cornelis,et al.  Fuzzy Association Rules: a Two-Sided Approach , 2003 .

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

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

[32]  Jf Baldwin,et al.  An Introduction to Fuzzy Logic Applications in Intelligent Systems , 1992 .

[33]  Maciej Wygralak.  Vaguely defined objects , 1995 .

[34]  Lotfi A. Zadeh,et al.  A COMPUTATIONAL APPROACH TO FUZZY QUANTIFIERS IN NATURAL LANGUAGES , 1983 .

[35]  D. S. Fernández.  Adquisición de relaciones entre atributos en bases de datos relacionales , 2000 .

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

[37]  Jian Pei,et al.  Mining frequent patterns without candidate generation , 2000, SIGMOD '00.

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

[39]  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).

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

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

[42]  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).

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

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

[45]  George J. Klir,et al.  Fuzzy sets, uncertainty and information , 1988 .

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

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

[48]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[49]  Cheng Yang,et al.  Efficient discovery of error-tolerant frequent itemsets in high dimensions , 2001, KDD '01.

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

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

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

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

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

[55]  Man Hon Wong,et al.  Finding Fuzzy Sets for the Mining of Fuzzy Association Rules for Numerical Attributes , 1998 .

[56]  Richard J. Roiger,et al.  Data Mining: A Tutorial Based Primer , 2002 .

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

[58]  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.

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

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

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

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

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