Prism: An effective approach for frequent sequence mining via prime-block encoding

Sequence mining is one of the fundamental data mining tasks. In this paper we present a novel approach for mining frequent sequences, called Prism. It utilizes a vertical approach for enumeration and support counting, based on the novel notion of primal block encoding, which in turn is based on prime factorization theory. Via an extensive evaluation on both synthetic and real datasets, we show that Prism outperforms popular sequence mining methods like SPADE [M.J. Zaki, SPADE: An efficient algorithm for mining frequent sequences, Mach. Learn. J. 42 (1/2) (Jan/Feb 2001) 31–60], PrefixSpan [J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal, M.-C. Hsu, PrefixSpan: Mining sequential patterns efficiently by prefixprojected pattern growth, in: Int'l Conf. Data Engineering, April 2001] and SPAM [J. Ayres, J.E. Gehrke, T. Yiu, J. Flannick, Sequential pattern mining using bitmaps, in: SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining, July 2002], by an order of magnitude or more.