Clicks: An effective algorithm for mining subspace clusters in categorical 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.

Authors: Zaki M. J., Peters M., Assent I., Seidl T.
Published in: Data & Knowledge Engineering (DKE) 60 (1)
Publisher: ACM - New York, NY, USA
Sprache: EN
Jahr: 2007
Seiten: 51-70
ISBN: 1-59593-135-X
DOI:10.1016/j.datak.2006.01.005
URL:DKE
Abstract
Typ: Zeitschriftenartikel
Forschungsgebiet: Data Analysis and Knowledge Extraction