How to "alternatize" a clustering algorithm

Authors:
M. Shahriar Hossain;Naren Ramakrishnan;Ian Davidson;Layne T. Watson
Affiliations:
Department of Mathematics and Computer Science, Virginia State University, Petersburg, USA 23806;Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, USA 24061;Department of Computer Science, University of California, Davis, USA 95616;Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, USA 24061 and Department of Mathematics, Virginia Polytechnic Institute and State University, Black ...
Venue:
Data Mining and Knowledge Discovery
Year:
2013

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Abstract

Given a clustering algorithm, how can we adapt it to find multiple, nonredundant, high-quality clusterings? We focus on algorithms based on vector quantization and describe a framework for automatic `alternatization' of such algorithms. Our framework works in both simultaneous and sequential learning formulations and can mine an arbitrary number of alternative clusterings. We demonstrate its applicability to various clustering algorithms--k-means, spectral clustering, constrained clustering, and co-clustering--and effectiveness in mining a variety of datasets.