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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/17391

    Title: Mining Strong Positive and Negative Sequential Patterns
    Other Titles: 探勘強勢正向及負向序列型樣
    Authors: Lin, Nancy P.;Chen, Hung-jen;Hao, Wei-hua;Chueh, Hao-en;Chang, Chung-i
    Contributors: 淡江大學軍訓室;淡江大學資訊工程學系
    Keywords: Data mining,Itemset,Frequent sequence,Positive sequential pattern,Negative sequential pattern,Strong sequential pattern
    Date: 2008-03
    Issue Date: 2009-08-12 13:59:58 (UTC+8)
    Publisher: Zographou: World Scientific and Engineering Academy and Society
    Abstract: In data mining field, sequential pattern mining can be applied in divers applications such as basket analysis, web access patterns analysis, and quality control in manufactory engineering, etc. Many methods have been proposed for mining sequential patterns. However, conventional methods only consider the occurrences of itemsets in customer sequences. The sequential patterns discovered by these methods are called as positive sequential patterns, i.e., such sequential patterns only represent the occurrences of itemsets. In practice, the absence of a frequent itemset in a sequence may imply significant information. We call a sequential pattern as negative sequential pattern, which also represents the absence of itemsets in a sequence. The two major difficulties in mining sequential patterns, especially negative ones, are that there may be huge number of candidates generated, and most of them are meaningless. In this paper, we proposed a method for mining strong positive and negative sequential patterns, called PNSPM. In our method, the absences of itemsets are also considered. Besides, only sequences with high degree of interestingness will be selected as strong sequential patterns. An example was taken to illustrate the process of PNSPM. The result showed that PNSPM could prune a lot of redundant candidates, and could extract meaningful sequential patterns from a large number of frequent sequences.
    Relation: WSEAS Transations on Computers 7(3), pp.119-124
    Appears in Collections:[Office of Military Education and Training] Journal Article
    [Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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