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On efficiently mining high utility sequential patterns

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Abstract

High utility sequential pattern mining is an emerging topic in pattern mining, which refers to identify sequences with high utilities (e.g., profits) but probably with low frequencies. To identify high utility sequential patterns, due to lack of downward closure property in this problem, most existing algorithms first generate candidate sequences with high sequence-weighted utilities (SWUs), which is an upper bound of the utilities of a sequence and all its supersequences, and then calculate the actual utilities of these candidates. This causes a large number of candidates since SWU is usually much larger than the real utilities of a sequence and all its supersequences. In view of this, we propose two tight utility upper bounds, prefix extension utility and reduced sequence utility, as well as two companion pruning strategies, and devise HUS-Span algorithm to identify high utility sequential patterns by employing these two pruning strategies. In addition, since setting a proper utility threshold is usually difficult for users, we also propose algorithm TKHUS-Span to identify top-k high utility sequential patterns by using these two pruning strategies. Three searching strategies, guided depth-first search (GDFS), best-first search (BFS) and hybrid search of BFS and GDFS, are also proposed to improve the efficiency of TKHUS-Span. Experimental results on some real and synthetic datasets show that HUS-Span and TKHUS-Span with strategy BFS are able to generate less candidate sequences and thus outperform other prior algorithms in terms of mining efficiency.

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Acknowledgments

The authors were supported by the Ministry of Science and Technology, Taiwan, under Project No. MOST 104-2221-E-032-037-MY2, MOST 103-2221-E-009-126-MY2, MOST 104-2918-I-009-003, MOST 104-2218-E-009-009 and MOST 104-2218-E-009-029.

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Authors and Affiliations

  1. Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan, ROC

    Jun-Zhe Wang & Jiun-Long Huang

  2. Department of Computer Science and Information Engineering, Tamkang University, New Taipei City, Taiwan, ROC

    Yi-Cheng Chen

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  1. Jun-Zhe Wang
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  2. Jiun-Long Huang
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  3. Yi-Cheng Chen
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Correspondence to Jiun-Long Huang.

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Wang, JZ., Huang, JL. & Chen, YC. On efficiently mining high utility sequential patterns. Knowl Inf Syst 49, 597–627 (2016). https://doi.org/10.1007/s10115-015-0914-8

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  • DOI: https://doi.org/10.1007/s10115-015-0914-8

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