Cluster-Based Evaluation in Fuzzy-Genetic Data Mining

Data mining is commonly used in attempts to induce association rules from transaction data. Most previous studies focused on binary-valued transaction data. Transactions in real-world applications, however, usually consist of quantitative values. In the past, we proposed a fuzzy-genetic data-mining algorithm for extracting both association rules and membership functions from quantitative transactions. It used a combination of large 1-itemsets and membership-function suitability to evaluate the fitness values of chromosomes. The calculation for large 1-itemsets could take a lot of time, especially when the database to be scanned could not totally fed into main memory. In this paper, an enhanced approach, called the cluster-based fuzzy-genetic mining algorithm, is thus proposed to speed up the evaluation process and keep nearly the same quality of solutions as the previous one. It divides the chromosomes in a population into clusters by the - means clustering approach and evaluates each individual according to both cluster and their own information. Experimental results also show the effectiveness and efficiency of the proposed approach.