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. Categories and Subject Descriptors: H.2.8 [Database Management]: Database Applications - Data Mining General Terms: Algorithms.

• Type: Conference
• Year: 2005
• Where: KDD
• Authors: Mohammed Javeed Zaki, Markus Peters, Ira Assent, Thomas Seidl
• Keywords: Algorithm Called Clicks, Clicks Mines Subspace, Clicks Outperforms, Data Mining, KDD 2005