Refining Pairwise Similarity Matrix for Cluster Ensemble Problem with Cluster Relations
Lecture notes in computer sciencePublished 1 January 2008
Natthakan Iam-On, Tossapon Boongoen, Simon Garrett
Citations71
SJR quartileQ2
SJR score0.35
SNIP0.55
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TL;DR
Two new link-based similarity matrices 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 to suggest higher clustering quality in comparison to their competitor.
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.
Keywords
Computer SciencePhysics and Astronomy
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