Refining Pairwise Similarity Matrix for Cluster Ensemble Problem with Cluster Relations

Authors:
Natthakan Iam-On;Tossapon Boongoen;Simon Garrett
Affiliations:
Department of Computer Science, Aberystwyth University, UK;Department of Computer Science, Aberystwyth University, UK;Department of Computer Science, Aberystwyth University, UK
Venue:
DS '08 Proceedings of the 11th International Conference on Discovery Science
Year:
2008

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Abstract

Cluster ensemble methods have recently emerged as powerful techniques, aggregating several input data clusterings to generate a single output clustering, with improved robustness and stability. This paper presents two new similarity matrices, which are empirically evaluated and compared against the standard co-association matrix on six datasets (both artificial and real data) using four different combination methods and six clustering validity criteria. In all cases, the results suggest the new link-based similarity matrices are able to extract efficiently the information embedded in the input clusterings, and regularly suggest higher clustering quality in comparison to their competitor.