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Prof. karam gouda :: Publications:

Mining sequential patterns in dense databases
Authors: Karam Gouda, Mosab Hassaan
Year: 2011
Keywords: Not Available
Journal: Journal of Database Management Systems (IJDMS)
Volume: 3
Issue: 1
Pages: 179-194
Publisher: Not Available
Local/International: International
Paper Link: Not Available
Full paper karam gouda_3111ijdms12.pdf
Supplementary materials Not Available

Sequential pattern mining is an important data mining problem with broad applications, including the analysis of customer purchase patterns, Web access patterns, DNA analysis, and so on. We show on dense databases, a typical algorithm like Spade algorithm tends to lose its efficiency. Spade is based on the used of lists containing the localization of the occurrences of pattern in the sequences and these lists are not appropriated in the case of dense databases. In this paper we present an adaptation of the wellknown diffset data representation [12] with Spade algorithm. The new version is called dSpade. Since diffset shows high performance for mining frequent itemsets in dense transactional databases, experimental evaluation shows that dSpade is suitable for mining dense sequence databases.

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