Share Email Print

Proceedings Paper

Clustering algorithms do not learn, but they can be learned
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

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)

© SPIE. Terms of Use
Back to Top