Abstract
In this work, we study the problem of closed sequential pattern mining. We propose a novel approach which extends a frequent sequence with closed itemsets instead of single items. The motivation is that closed sequential patterns are composed of only closed itemsets. Hence, unnecessary item extensions which generates non-closed sequential patterns can be avoided. Experimental evaluation shows that the proposed approach is two orders of magnitude faster than previous works with a modest memory cost.
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© 2006 Springer-Verlag Berlin Heidelberg
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Huang, KY., Chang, CH., Tung, JH., Ho, CT. (2006). COBRA: Closed Sequential Pattern Mining Using Bi-phase Reduction Approach. In: Tjoa, A.M., Trujillo, J. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2006. Lecture Notes in Computer Science, vol 4081. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11823728_27
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DOI: https://doi.org/10.1007/11823728_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37736-8
Online ISBN: 978-3-540-37737-5
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