A Cluster-Based Fuzzy-Genetic Mining Approach for Association Rules and Membership Functions

Data mining is most commonly used in attempts to induce association rules from transaction data. Transactions in real-world applications, however, usually consist of quantitative values. Designing a sophisticated data-mining algorithm able to deal with various types of data presents a challenge to workers in this research field. In this paper, a cluster-based fuzzy-genetic mining algorithm is proposed for extracting both fuzzy association rules and membership functions from quantitative transactions. The proposed algorithm can dynamically adjust membership functions by genetic algorithms and uses them to fuzzify quantitative transactions. It can also speed up the evaluation process and keep good quality of solutions by clustering chromosomes. Experimental results show the effectiveness of the proposed approach.