CLICKS: Mining Subspace Clusters in Categorical Data via K-Partite Maximal Cliques

I
ICDE
2005
21st International Conference on Data Engineering (ICDE'05)
Abstract - CLICKS: Mining Subspace Clusters in Categorical Data via K-Partite Maximal Cliques

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21st International Conference on Data Engineering (ICDE'05)

CLICKS: Mining Subspace Clusters in Categorical Data via K-Partite Maximal Cliques (PDF)

Tokyo, Japan

April 05-April 08

ISBN: 0-7695-2285-8

	ASCII Text	x
	Mohammed J. Zaki, Markus Peters, "CLICKS: Mining Subspace Clusters in Categorical Data via K-Partite Maximal Cliques," Data Engineering, International Conference on, pp. 355-356, 21st International Conference on Data Engineering (ICDE'05), 2005.

	BibTex	x
	@article{ 10.1109/ICDE.2005.33, author = {Mohammed J. Zaki and Markus Peters}, title = {CLICKS: Mining Subspace Clusters in Categorical Data via K-Partite Maximal Cliques}, journal ={Data Engineering, International Conference on}, volume = {0}, year = {2005}, issn = {1084-4627}, pages = {355-356}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDE.2005.33}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Engineering, International Conference on TI - CLICKS: Mining Subspace Clusters in Categorical Data via K-Partite Maximal Cliques SN - 1084-4627 SP355 EP356 A1 - Mohammed J. Zaki, A1 - Markus Peters, PY - 2005 KW - null VL - 0 JA - Data Engineering, International Conference on ER -

Mohammed J. Zaki, Rensselaer Polytechnic Institute

Markus Peters, Rensselaer Polytechnic Institute

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDE.2005.33

We present a novel algorithm called CLICKS, that finds clusters in categorical datasets based on a search for k-partite maximal cliques. Unlike previous methods, CLICKS mines subspace clusters. It uses a selective vertical method to guarantee complete search. CLICKS outperforms previous approaches by over an order of magnitude and scales better than any of the existing method for high-dimensional datasets. We demonstrate this improvement in an excerpt from our comprehensive performance studies.

Citation:

Mohammed J. Zaki, Markus Peters, "CLICKS: Mining Subspace Clusters in Categorical Data via K-Partite Maximal Cliques," icde, pp.355-356, 21st International Conference on Data Engineering (ICDE'05), 2005

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