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.