CLICKS: an effective algorithm for mining subspace clusters in categorical datasets

Authors:
Mohammed J. Zaki;Markus Peters;Ira Assent;Thomas Seidl
Affiliations:
Rensselaer Polytechnic Institute, Troy, NY;RWTH University, Aachen, Germany;RWTH University, Aachen, Germany;RWTH University, Aachen, Germany
Venue:
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
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.