Mining High Utility Sequential Patterns Using Sequence Utility Lists


KIPS Transactions on Software and Data Engineering, Vol. 7, No. 2, pp. 51-62, Feb. 2018
10.3745/KTSDE.2018.7.2.51,   PDF Download:
Keywords: High Utility Sequential Pattern Mining, Sequence Utility List, Candidate Pattern Pruning
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.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from September 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article
[IEEE 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.2018.7.2.51.

[ACM Style]
Jong Soo Park. 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.2018.7.2.51.