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CLICKS: Mining Subspace Clusters in Categorical Data via K-Partite Maximal Cliques
Tokyo, Japan April 05-April 08
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDE.2005.3321st International Conference on Data ...
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Mohammed J. Zaki, Rensselaer Polytechnic Institute
Markus Peters, Rensselaer Polytechnic Institute
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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