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Proceedings Paper

Clustering algorithms do not learn, but they can be learned
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Paper Abstract

Pattern classification theory involves an error criterion, optimal classifiers, and a theory of learning. For clustering, there has historically been little theory; in particular, there has generally (but not always) been no learning. The key point is that clustering has not been grounded on a probabilistic theory. Recently, a clustering theory has been developed in the context of random sets. This paper discusses learning within that context, in particular, k- nearest-neighbor learning of clustering algorithms.

Paper Details

Date Published: 30 August 2005
PDF: 9 pages
Proc. SPIE 5916, Mathematical Methods in Pattern and Image Analysis, 59160T (30 August 2005); doi: 10.1117/12.617418
Show Author Affiliations
Marcel Brun, Translational Genomics Research Institute (United States)
Edward R. Dougherty, Translational Genomics Research Institute (United States)
Texas A&M Univ. (United States)

Published in SPIE Proceedings Vol. 5916:
Mathematical Methods in Pattern and Image Analysis
Jaakko T. Astola; Ioan Tabus; Junior Barrera, Editor(s)

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