期刊名称：International Journal on Computer Science and Engineering
印刷版ISSN：2229-5631
电子版ISSN：0975-3397
出版年度：2014
卷号：6
期号：07
页码：221-231
出版社：Engg Journals Publications
摘要：In data mining research, generating frequent items from large databases is one of the important issues and the key factor for implementing association rule mining tasks. Mining infrequent items such as relationships among rare but expensive products is another demanding issue which have been shownin some recent studies. Therefore this study considers user assigned threshold values as a constraint which helps users mine those rules which are more interesting for them. In addition, in real world users may prefer to know relationships among frequent items along with infrequent ones. The particle swarm optimization algorithm is an important heuristic technique in recent years and this study uses this technique to mine association rules effectively. If this technique considers user defined threshold values, interesting association rules can be generated more efficiently. Therefore this study proposes a novel approach which includes using particle swarm optimization algorithm to mine association rules from databases. Our implementation of the search strategy includes bitmap representation of nodes in a lexicographic tree and from superset-subset relationship of the nodes it classifies frequent items along with infrequent itemsets. In addition, this approach avoids extra calculation overhead for generating frequent pattern trees and handling large memory which store the support values of candidate itemets. Our experimental results show that this approach efficiently mines association rules. It accesses a database to calculate a support value for fewer numbers of nodes to find frequent itemsets and from that it generates association rules, which dramatically reduces search time. The main aim of this proposed algorithm is to show how heuristic method works on real databases to find all the interesting association rules in an efficient way.
关键词：Particle Swarm Optimization; Data mining; genetic algorithm;frequent itemsets;lexicographic tree.
