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

摘要

We present a novel algorithm called C_(LICKS), that finds clusters in categorical datasets based on a search for k-partite maximal cliques. Unlike previous methods, C_(LICKS) mines subspace clusters. It uses a selective vertical method to guarantee complete search. C_(LICKS) 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.