Clicks: An effective algorithm for mining subspace clusters in categorical datasets
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge …, 2005•dl.acm.org
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.
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.
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.
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