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    題名: On efficiently mining high utility sequential patterns
    作者: Wang, Jun-Zhe;Huang, Jiun-Long;Chen, Yi-Cheng
    關鍵詞: High utility sequential pattern;High utility sequential pattern mining;Top-k high utility sequential pattern;Utility mining
    日期: 2016-10-11
    上傳時間: 2016-11-30 02:10:47 (UTC+8)
    出版者: Springer
    摘要: 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.
    關聯: Knowledge and Information Systems 49(2), pp. 597-627
    DOI: 10.1007/s10115-015-0914-8
    顯示於類別:[資訊工程學系暨研究所] 期刊論文

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