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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/29668


    Title: On mining multi-time-interval sequential patterns
    Authors: Hu,YH;Huang,TCK;Yang,HR;Chen,YL
    Contributors: 資訊管理研究所
    Keywords: TRAVERSAL PATTERNS;LARGE DATABASES;SEQUENCES;ALGORITHM
    Date: 2009
    Issue Date: 2010-06-29 20:37:47 (UTC+8)
    Publisher: 中央大學
    Abstract: Sequential pattern mining is essential in many applications, including computational biology, consumer behavior analysis, web log analysis, etc. Although sequential patterns can tell us what items are frequently to be purchased together and in what order, they cannot provide information about the time span between items for decision support. Previous studies dealing with this problem either set time constraints to restrict the patterns discovered or define time-intervals between two successive items to provide time information. Accordingly, the first approach falls short in providing clear time-interval information while the second cannot discover time-interval information between two non-successive items in a sequential pattern. To provide more time-related knowledge, we define a new variant of time-interval sequential patterns, called multi-time-interval sequential patterns, which can reveal the time-intervals between all pairs of items in a pattern. Accordingly, we develop two efficient algorithms, called the MI-Apriori and MI-PrefixSpan algorithms, to solve this problem. The experimental results show that the MI-PrefixSpan algorithm is faster than the MI-Apriori algorithm. but the MI-Apriori algorithm has better scalability in long sequence data. (C) 2009 Elsevier B.V. All rights reserved.
    Relation: DATA & KNOWLEDGE ENGINEERING
    Appears in Collections:[資訊管理研究所] 期刊論文

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