A Closed Itemset Property based Multi-objective Evolutionary Approach for Mining Frequent and High Utility Itemsets

Heng Cao, Shangshang Yang, Qingren Wang, Qijun Wang, Lei Zhang

Abstract: Mining frequent and high utility itemsets from a transactional database is a significant task in the field of data mining and has attracted increasing attention in the past several years. Recently, researchers focus on designing multiobjective evolutionary algorithms (MOEAs) for the task of mining frequent and high utility itemsets, which has shown promising performance. In this paper, we continue this research line by further exploring the potential of MOEAs for mining frequent and high utility itemsets. Tb be specific, we suggest a closed itemset property based multi-objective evolutionary approach, termed as CP-MOEA, where two individual updating strategies are designed for improving the quality of mining frequent and high utility itemsets. We find that if the superset of an itemset is closed, then this itemset must be dominated by its superset, termed as closed itemset property. The proposed two individual updating strategies exploit this property of closed itemset to guide the evolution of the population at certain times. The experimental results on six real datasets demonstrate the effectiveness of the proposed algorithm CP-MOEA comparing to the state-of-the-art baseline.