Fuzzy Association Rules: Semantic Issues and Quality Measures

Fuzzy association rules provide a data mining tool which is especially interesting from a knowledge-representational point of view since fuzzy attribute values allow for expressing rules in terms of natural language. So far, however, association rules of this type have not been investigated thoroughly from a semantical point of view. Particularly, the quality measures which have been proposed for assessing such rules are mostly "ad-hoc" generalizations of measures for classical rules. In this paper, we show that fuzzy association rules can be interpreted in different ways and that the interpretation has a strong influence on their assessment and, hence, on the process of rule mining. We motivate the use of multiple-valued implication operators in order to model fuzzy association rules and propose quality measures suitable for this type of rule.

[1]  Didier Dubois,et al.  Merging Fuzzy Information , 1999 .

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

[3]  Jennifer Widom,et al.  Clustering association rules , 1997, Proceedings 13th International Conference on Data Engineering.

[4]  D. Dubois,et al.  Weighted fuzzy pattern matching , 1988 .

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

[6]  Henri Prade,et al.  What are fuzzy rules and how to use them , 1996, Fuzzy Sets Syst..

[7]  Witold Pedrycz,et al.  Data mining and fuzzy modeling , 1996, Proceedings of North American Fuzzy Information Processing.

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

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

[10]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

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

[12]  Settimo Termini,et al.  Entropy of L-Fuzzy Sets , 1974, Inf. Control..

[13]  Shamkant B. Navathe,et al.  An Efficient Algorithm for Mining Association Rules in Large Databases , 1995, VLDB.

[14]  Henri Prade,et al.  Estimations of Expectedness and potential Surprise in Possibility Theory , 1994, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[15]  A. Jaoua,et al.  Mining linguistic summaries of databases using based Lukasiewicz implication fuzzy functional dependency , 1999, FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315).

[16]  Attila Gyenesei,et al.  A Fuzzy Approach for Mining Quantitative Association Rules , 2000, Acta Cybern..

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

[18]  Ronald R. Yager,et al.  Aggregating evidence using quantified statements , 1985, Inf. Sci..

[19]  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).

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

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

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

[23]  H. Prade,et al.  Toward the representation of implication-based fuzzy rules in terms of crisp rules , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[24]  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).

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

[26]  Mukesh Singhal,et al.  Fuzzy Functional Dependencies and Fuzzy Association Rules , 1999, DaWaK.