Clustering algorithms do not learn, but they can be learned

Marcel Brun; Edward R. Dougherty

doi:10.1117/12.617418

30 August 2005 Clustering algorithms do not learn, but they can be learned

Marcel Brun, Edward R. Dougherty

Proceedings Volume 5916, Mathematical Methods in Pattern and Image Analysis; 59160T (2005) https://doi.org/10.1117/12.617418
Event: Optics and Photonics 2005, 2005, San Diego, California, United States

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.

Citation Download Citation

Marcel Brun and Edward R. Dougherty "Clustering algorithms do not learn, but they can be learned", Proc. SPIE 5916, Mathematical Methods in Pattern and Image Analysis, 59160T (30 August 2005); https://doi.org/10.1117/12.617418

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