关键词: 聚类算法, 关联规则, 模糊集, 遗传算法, k-means算法

Abstract: Through analysis of characteristics of continuous attributes and the existing mining algorithms based on association rules，the further research is conducted on the quantitative description accuracy and the algorithm efficiency，and a clustering-based fuzzy genetic association-rule mining algorithm is proposed，aiming to solve the “slow-speed” issue of the existing fuzzy genetic mining algorithm which computes the chromosome adaptive value based on the combination of large1-itemsets and membership-function suitability.This algorithm adopts fuzzy genetic principles，extracting association rules and membership-functions simultaneously from the transaction data.Meanwhile，it adopts k-means clustering algorithm to categorize the chromosomes of a particular species.And based on the categorization information and its own information，the algorithm evaluates the adaptability of each chromosome.The testing results indicate the fastness and accuracy of this algorithm.

Key words: cluster algorithm, association rules, fuzzy set, genetic algorithm, k-means algorithm