Generating multiple alternative clusterings via globally optimal subspaces

Authors:
Xuan Hong Dang;James Bailey
Affiliations:
Department of Computer Science, Aarhus University, Aarhus N, Denmark 8200;Department of Computing and Information Systems, The University of Melbourne, Melbourne, Australia 3010
Venue:
Data Mining and Knowledge Discovery
Year:
2014

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Abstract

Clustering analysis is important for exploring complex datasets. Alternative clustering analysis is an emerging subfield involving techniques for the generation of multiple different clusterings, allowing the data to be viewed from different perspectives. We present two new algorithms for alternative clustering generation. A distinctive feature of our algorithms is their principled formulation of an objective function, facilitating the discovery of a subspace satisfying natural quality and orthogonality criteria. The first algorithm is a regularization of the Principal Components analysis method, whereas the second is a regularization of graph-based dimension reduction. In both cases, we demonstrate a globally optimum subspace solution can be computed. Experimental evaluation shows our techniques are able to equal or outperform a range of existing methods.