Mining Frequent Closed Patterns in Microarray Data

Brighton, United Kingdom November 01-November 04

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http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10070

Fourth IEEE International Conference ...

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	ASCII Text	x
	Gao Cong, Kian-Lee Tan, Anthony K. H. Tung, Feng Pan, "Mining Frequent Closed Patterns in Microarray Data," Data Mining, IEEE International Conference on, pp. 363-366, Fourth IEEE International Conference on Data Mining (ICDM'04), 2004.

	BibTex	x
	@article{ 10.1109/ICDM.2004.10070, author = {Gao Cong and Kian-Lee Tan and Anthony K. H. Tung and Feng Pan}, title = {Mining Frequent Closed Patterns in Microarray Data}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2004}, isbn = {0-7695-2142-8}, pages = {363-366}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10070}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - Mining Frequent Closed Patterns in Microarray Data SN - 0-7695-2142-8 SP363 EP366 A1 - Gao Cong, A1 - Kian-Lee Tan, A1 - Anthony K. H. Tung, A1 - Feng Pan, PY - 2004 KW - null VL - 0 JA - Data Mining, IEEE International Conference on ER -

Gao Cong, National University of Singapore

Kian-Lee Tan, National University of Singapore

Anthony K. H. Tung, National University of Singapore

Feng Pan, National University of Singapore

Microarray data typically contains a large number of columns and a small number of rows, which poses a great challenge for existing frequent (closed) pattern mining algorithms that discover patterns in item enumeration space. In this paper, we propose two new algorithms that explore the row enumeration space to mine frequent closed patterns. Several experiments on real-life gene expression data show that the new algorithms are faster than existing algorithms, including CLOSET, CHARM, CLOSET+ and CARPENTER.

Citation:

Gao Cong, Kian-Lee Tan, Anthony K. H. Tung, Feng Pan, "Mining Frequent Closed Patterns in Microarray Data," icdm, pp.363-366, Fourth IEEE International Conference on Data Mining (ICDM'04), 2004

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