Abstract
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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