挖掘最大频繁项集的遗传蚁群优化算法  

Genetic ant colony optimization for mining maximal frequent itemsets

作　　者：黄红星[1] 黄习培[1] 王秀丽[1] 

机构地区：[1]福建农林大学计算机与信息学院,福州350002

出　　处：《计算机应用研究》2010年第7期2505-2508,共4页Application Research of Computers

摘　　要：为了提高挖掘的效率和精度,采用代数定义最大频繁项集并建立其数学模型,通过二进制编码将支持度的计算、蚁群算法和遗传算法求解有机地融合,从而提出一种求解该数学模型的遗传蚁群算法。实验表明,该算法挖掘最大频繁项集是有效的,具有良好的伸缩性。In order to improve the efficiency and accuracy of mining,adopted the algebraic definition for maximal frequent itemsets and established the mathematical model for it.The computing of support,ant colony algorithm and genetic algorithm were merged organically by the binary code.Thus,this paper proposed a genetic ant colony algorithm to solve this mathematical model.Experimental results show that the proposed algorithm for mining maximal frequent itemsets is effective and scalable.

关 键 词：关联规则 最大频繁项集 遗传算法 蚁群算法 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]