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Home > Archive>Volume 14, Issue 5, 2003 >970-975
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Discovery of Generalized Local Sequential Patterns
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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
    [1]Srikant R, Agrawal R. Mining sequential patterns: Generalizations and performance improvemets. In: Proceedings of the 5th International Conference on Extending Database Technology. France, 1996.
    [2]Wang K. Discovering patterns from large and dynamic sequential data. Special Issues on Data Mining and Knowledge Discovery, Journal of Intelligent Infrmation Systems, 1997,9(1):8～33.
    [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.
    [6]Han J, Dong G, Yin Y. Efficient mining of partial periodic patterns in time series database. In: Proceedings of the 15th International Conference on Data Engineering. IEEE Computer Society, 1999. 106～115.
    [7]Jin X, Wang L, Lu Y, Shi C. Indexing and mining of the local patterns in sequence database. In: Proceedings of the IDEAL 2002. Springer-Verlag, 2002.68～73.
    [8]Jin X, Lu Y, Shi C. Distribution discovery: Local analysis of temporal rules. In: Proceedings of the PAKDD 2002. Taibei:Springer Verlag, 2002.469～480.
    [9]Weiner P. Linear pattern matching algorithms. In: Proceedings of the 14th IEEE Annual Symposium on Switching and Automata Theory. 1973.
    [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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