30 August 2005 Clustering algorithms do not learn, but they can be learned
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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.
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Marcel Brun, 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); doi: 10.1117/12.617418; https://doi.org/10.1117/12.617418
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