An indexed set representation based multi-objective evolutionary approach for mining diversified top-k high utility patterns

How to discover top-k patterns with the largest utility values, namely, mining top-k high utility patterns, is a hot topic in data mining. However, most of the existing works for mining top-k high utility patterns consider each pattern separately during the mining process, thus many mined patterns are highly similar and lack diversity. In this paper, we propose to mine top-k high utility patterns with high diversity for enhancing users'satisfaction in recommendation. Specifically, we first introduce a simple measure of coverage to quantify the diversity of the whole set, that is, the top-k patterns as a complete entity. Then we propose an i ndexed s et r epresentation based m ulti-o bjective e volutionary a pproach named ISR-MOEA to mine diversified top-k high utility patterns, due to the fact that the two measures utility and coverage are conflicting. In ISR-MOEA, an indexed set individual representation scheme is suggested for fast encoding and decoding the top-k pattern set. Experimental results on six real-world and two synthetic datasets demonstrate the effectiveness of the proposed approach. The proposed approach can obtain several groups of top-k pattern set with different trade-offs between utility and diversity in only one run, which would further enhance the satisfaction of users.