Multi-objective genetic algorithm based approaches for mining optimized fuzzy association rules

Abstract.Association rules form one of the most widely used techniques to discover correlations among attribute in a database. So far, some efficient methods have been proposed to obtain these rules with respect to an optimal goal, such as: to maximize the number of large itemsets and interesting rules or the values of support and confidence for the discovered rules. This paper first introduces optimized fuzzy association rule mining in terms of three important criteria; strongness, interestingness and comprehensibility. Then, it proposes multi-objective Genetic Algorithm (GA) based approaches for discovering these optimized rules. Optimization technique according to given criterion may be one of two different forms; The first tries to determine the appropriate fuzzy sets of quantitative attributes in a prespecified rule, which is also called as certain rule. The second deals with finding both uncertain rules and their appropriate fuzzy sets. Experimental results conducted on a real data set show the effectiveness and applicability of the proposed approach.

[1]  Abraham Silberschatz,et al.  What Makes Patterns Interesting in Knowledge Discovery Systems , 1996, IEEE Trans. Knowl. Data Eng..

[2]  Jaideep Srivastava,et al.  Selecting the right interestingness measure for association patterns , 2002, KDD.

[3]  Reda Alhajj,et al.  A clustering algorithm with genetically optimized membership functions for fuzzy association rules mining , 2003, The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03..

[4]  Kyuseok Shim,et al.  Mining optimized support rules for numeric attributes , 2001, Inf. Syst..

[5]  Edward Omiecinski,et al.  Alternative Interest Measures for Mining Associations in Databases , 2003, IEEE Trans. Knowl. Data Eng..

[6]  Alex A. Freitas,et al.  Discovering comprehensible classification rules with a genetic algorithm , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

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

[8]  Jiawei Han,et al.  Efficient and Effective Clustering Methods for Spatial Data Mining , 1994, VLDB.

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

[10]  Reda Alhajj,et al.  Facilitating fuzzy association rules mining by using multi-objective genetic algorithms for automated clustering , 2003, Third IEEE International Conference on Data Mining.

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

[12]  Roberto J. Bayardo,et al.  Mining the most interesting rules , 1999, KDD '99.

[13]  Yasuhiko Morimoto,et al.  Data mining using two-dimensional optimized association rules: scheme, algorithms, and visualization , 1996, SIGMOD '96.

[14]  Alex Alves Freitas,et al.  On rule interestingness measures , 1999, Knowl. Based Syst..

[15]  Wynne Hsu,et al.  Pruning and summarizing the discovered associations , 1999, KDD '99.

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

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

[18]  Balaji Padmanabhan,et al.  Small is beautiful: discovering the minimal set of unexpected patterns , 2000, KDD '00.

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

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

[21]  Reda Alhajj,et al.  Efficient Automated Mining of Fuzzy Association Rules , 2002, DEXA.

[22]  Bhabesh Nath,et al.  Multi-objective rule mining using genetic algorithms , 2004, Inf. Sci..

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

[24]  Wynne Hsu,et al.  Analyzing the Subjective Interestingness of Association Rules , 2000, IEEE Intell. Syst..

[25]  Yasuhiko Morimoto,et al.  Mining optimized association rules for numeric attributes , 1996, J. Comput. Syst. Sci..

[26]  Attila Gyenesei,et al.  Interestingness Measures for Fuzzy Association Rules , 2001, PKDD.

[27]  Kyuseok Shim,et al.  Mining Optimized Association Rules with Categorical and Numeric Attributes , 2002, IEEE Trans. Knowl. Data Eng..

[28]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..