An Evolutionary Autonomous Agents Approach to Frequent Itemsets Mining
This paper proposes a new approach to frequent itemsets mining which utilizes evolutionary autonomous agents. The optimaliry of frequent itemsets extraction is to find all the frequent itemsets in the item set space whose support values are greater than the support threshold. In the presented approach, the autonomous agents, being distributed computational entities, operate in N-D lattice of the itemset space and exhibit a number of reactive behaviors. In order to effectively locate the frequent itemsets, the agents were designed to sense the local stimuli from their environment by means of evaluating the support value of local points, and accordingly activate their behaviors. The experiment results have shown that it is efficient in dealing with the problem on the complexity of the rule search space.
evolutionary computation frequent itemset agent
Hong Liang Wang Li Zhao
Department of computer Shijiazhuang vocational technology institute Shijiazhuang, China School of computer science and engineering Xi an university of technology Xi an, China
国际会议
成都
英文
258-262
2010-12-17（万方平台首次上网日期，不代表论文的发表时间）