An Efficient Algorithm for High Utility Sequential Pattern Mining

Abstract

High utility sequential pattern mining is to mine sequences with high utility (e. g. profits) but probably with low frequency. In some applications such as marketing analysis, high utility sequential patterns are usually more useful than sequential patterns with high frequency. In this paper, we devise two pruning strategies RSU and PDU, and propose HUS-Span algorithm based on these two pruning strategies to efficiently identify high utility sequential patterns. Experimental results show that HUS-Span algorithm outperforms prior algorithms by pruning more low utility sequences.