淡江大學機構典藏:Item 987654321/17391
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    题名: Mining Strong Positive and Negative Sequential Patterns
    其它题名: 探勘強勢正向及負向序列型樣
    作者: Lin, Nancy P.;Chen, Hung-jen;Hao, Wei-hua;Chueh, Hao-en;Chang, Chung-i
    贡献者: 淡江大學軍訓室;淡江大學資訊工程學系
    关键词: Data mining,Itemset,Frequent sequence,Positive sequential pattern,Negative sequential pattern,Strong sequential pattern
    日期: 2008-03
    上传时间: 2009-08-12 13:59:58 (UTC+8)
    出版者: Zographou: World Scientific and Engineering Academy and Society
    摘要: 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.
    關聯: WSEAS Transations on Computers 7(3), pp.119-124
    显示于类别:[軍訓室] 期刊論文
    [資訊工程學系暨研究所] 期刊論文

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