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CLICKS: An Effective Algorithm for Mining Subspace Clusters in Categorical Datasets (2005)
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by
Mohammed J. Zaki
,
Markus Peters
,
Ira Assent
,
Thomas Seidl
| Citations: | 19 - 4 self |
BibTeX
@MISC{Zaki05clicks:an,
author = {Mohammed J. Zaki and Markus Peters and Ira Assent and Thomas Seidl},
title = { CLICKS: An Effective Algorithm for Mining Subspace Clusters in Categorical Datasets},
year = {2005}
}
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Abstract
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. These results are demonstrated in a comprehensive performance study on real and synthetic datasets.
