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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/108575

    Title: On efficiently mining high utility sequential patterns
    Authors: Wang, Jun-Zhe;Huang, Jiun-Long;Chen, Yi-Cheng
    Keywords: High utility sequential pattern;High utility sequential pattern mining;Top-k high utility sequential pattern;Utility mining
    Date: 2016-10-11
    Issue Date: 2016-11-30 02:10:47 (UTC+8)
    Publisher: Springer
    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.
    Relation: Knowledge and Information Systems 49(2), pp. 597-627
    DOI: 10.1007/s10115-015-0914-8
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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