Non-redundant multiple clustering by nonnegative matrix factorization

Abstract

Clustering is one of the basic tasks in data mining and machine learning which aims at discovering hidden structure in the data. For many real-world applications, there often exist many different yet meaningful clusterings while most of existing clustering methods only produce a single clustering. To address this limitation, multiple clustering, which tries to generate clusterings that are high quality and different from each other, has emerged recently. In this paper, we propose a novel alternative clustering method that generates non-redundant multiple clusterings sequentially. The algorithm is built upon nonnegative matrix factorization, and we take advantage of the nonnegative property to enforce the non-redundancy. Specifically, we design a quadratic term to measure the redundancy between the reference clustering and the new clustering, and incorporate it into the objective. The optimization problem takes on a very simple form, and can be solved efficiently by multiplicative updating rules. Experimental results demonstrate that the proposed algorithm is comparable to or outperforms existing multiple clustering methods.