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    Abstract:

    Previous work on pattern discovery in sequence data mainly considers finding global patterns, whereevery record in the temporal sequence contributes to support the patterns. However, local patterns, which arefrequent only in some time periods, are actually very common in practice and the efficient discovery of it ispotentially very useful. This paper presents a method for discovering generalized local sequential patterns with thestructure that supports efficiently locating and counting of the pattern instances and a two-phase method forefficiently mining of local patterns. Experimental results corresponded with the problem definition and verified thesuperiority of the approach.

    Reference
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    [3]Li Y, Wang XS, Jajodia S. Discovering temporal patterns in multiple granularities. International Workshop on Temporal, Spatial and Spatio-Temporal Data Mining. Lyon, France. 2000.
    [4]Kam P, Fu AWC. Discovering temporal patterns for interval-based events. In: Proceedings of the 2nd International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2000). UK, 2000.
    [5]Chen X, Petrounias I. An integrated query and mining system for temporal association rules. In: Proceedingsof the 2nd International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2000). UK, 2000. 327~336.
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    [10]Spiliopoulou M, Roddick JF. Higher order mining: Modeling and mining the results of knowledge discovery. In: Proceedings ofthe 2nd International Conference on Data Mining Methods and Databases, Data Mining II. 2000.
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靳晓明,陆玉昌,石纯一.序列中的一般化局部序列模式发现.软件学报,2003,14(5):970-975

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History
  • Received:June 04,2002
  • Revised:August 23,2002
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