An Efficient Representation for Genetic-Fuzzy Mining of Association Rules

Data mining is a blooming area in information science. Mining association rules aims to find the relationship among items in the databases and has become one of the most important data mining technologies. Previous study shows the capability of genetic algorithm (GA) to find the membership functions for fuzzy data mining. However, the chromosome representation cannot avoid the occurrence of inappropriate arrangement of membership functions, resulting in inefficiency of GA in searching for the optimal membership functions. This study proposes a novel representation that takes advantage of the structure information of membership functions to deal with the issue. In the light of overlap and coverage, we propose two heuristics for appropriate arrangement of membership functions. The experimental results show that GA using the proposed representation can achieve high fitness and suitability. The results also indicate that the two heuristics help to well exploit the structure information and therefore enhance GA in terms of solution quality and convergence speed on fuzzy association rules mining.

[1]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[2]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[3]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[4]  Francisco Herrera,et al.  Fuzzy connectives based crossover operators to model genetic algorithms population diversity , 1997, Fuzzy Sets Syst..

[5]  Tzung-Pei Hong,et al.  A GA-based Fuzzy Mining Approach to Achieve a Trade-off Between Number of Rules and Suitability of Membership Functions , 2006, Soft Comput..

[6]  Chun Zhang,et al.  Storing and querying ordered XML using a relational database system , 2002, SIGMOD '02.

[7]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[8]  Yaochu Jin,et al.  Surrogate-assisted evolutionary computation: Recent advances and future challenges , 2011, Swarm Evol. Comput..

[9]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..

[10]  Tzung-Pei Hong,et al.  Genetic-Fuzzy Data Mining With Divide-and-Conquer Strategy , 2008, IEEE Transactions on Evolutionary Computation.

[11]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

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

[13]  Evangelos Simoudis,et al.  An Overview of Issues in Developing Industrial Data Mining and Knowledge Discovery Applications , 1996, KDD.

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

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

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

[17]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery: An Overview , 1996, Advances in Knowledge Discovery and Data Mining.

[18]  Claire Cardie,et al.  Constrained K-means Clustering with Background Knowledge , 2001, ICML.

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