Mining High Utility Sequential Patterns Using Sequence Utility Lists

KIPS Transactions on Software and Data Engineering, Vol. 7, No.2, pp.51-62, February 2018
10.3745/KTSDE.2018.7.2.051, Full Text

Abstract

High utility sequential pattern (HUSP) mining has been considered as an important research topic in data mining. Although some algorithms have been proposed for this topic, they incur the problem of producing a large search space for HUSPs. The tighter utility upper bound of a sequence can prune more unpromising patterns early in the search space. In this paper, we propose a sequence expected utility (SEU) as a new utility upper bound of each sequence, which is the maximum expected utility of a sequence and all its descendant sequences. A sequence utility list for each pattern is used as a new data structure to maintain essential information for mining HUSPs. We devise an algorithm, high sequence utility list-span (HSUL-Span), to identify HUSPs by employing SEU. Experimental results on both synthetic and real datasets from different domains show that HSUL-Span generates considerably less candidate patterns and outperforms other algorithms in terms of execution time.


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Cite this paper

[KIPS Transactions Style]
J. S. Park, "Mining High Utility Sequential Patterns Using Sequence Utility Lists," KIPS Transactions on Software and Data Engineering, Vol.7, No.2, pp.51-62, 2018, DOI: 10.3745/KTSDE.2018.7.2.051.

[IEEE Style]
Jong Soo Park, "Mining High Utility Sequential Patterns Using Sequence Utility Lists," KIPS Transactions on Software and Data Engineering, vol. 7, no. 2, pp. 51-62, 2018. DOI: 10.3745/KTSDE.2018.7.2.051.

[ACM Style]
Park, J. S. 2018. Mining High Utility Sequential Patterns Using Sequence Utility Lists. KIPS Transactions on Software and Data Engineering, 7, 2, (2018), 51-62. DOI: 10.3745/KTSDE.2018.7.2.051.