Prism: A Primal-Encoding Approach for Frequent Sequence Mining

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http://doi.ieeecomputersociety.org/10.1109/ICDM.2007.33

2007 Seventh IEEE International Confe ...

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	Karam Gouda, Mosab Hassaan, Mohammed J. Zaki, "Prism: A Primal-Encoding Approach for Frequent Sequence Mining," Data Mining, IEEE International Conference on, pp. 487-492, 2007 Seventh IEEE International Conference on Data Mining, 2007.

	BibTex	x
	@article{ 10.1109/ICDM.2007.33, author = {Karam Gouda and Mosab Hassaan and Mohammed J. Zaki}, title = {Prism: A Primal-Encoding Approach for Frequent Sequence Mining}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2007}, issn = {1550-4786}, pages = {487-492}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2007.33}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

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	TY - CONF JO - Data Mining, IEEE International Conference on TI - Prism: A Primal-Encoding Approach for Frequent Sequence Mining SN - 1550-4786 SP487 EP492 A1 - Karam Gouda, A1 - Mosab Hassaan, A1 - Mohammed J. Zaki, PY - 2007 VL - 0 JA - Data Mining, IEEE International Conference on ER -

Karam Gouda

Mosab Hassaan

Mohammed J. Zaki

Sequence mining is one of the fundamental data mining tasks. In this paper we present a novel approach called PRISM, for mining frequent sequences. PRISM utilizes a vertical approach for enumeration and support counting, based on the novel notion of prime 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 [10], PrefixSpan [6] and SPAM [2], by an order of magnitude or more.

Citation:

Karam Gouda, Mosab Hassaan, Mohammed J. Zaki, "Prism: A Primal-Encoding Approach for Frequent Sequence Mining," icdm, pp.487-492, 2007 Seventh IEEE International Conference on Data Mining, 2007

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