Normality-based validation for crisp clustering

A new validity index for crisp clustering that is based on the average normality of the clusters that provides better results than other indices, both with respect to the prediction of the correct number of clusters and to the similarity among the real clusters and those inferred.

Abstract

This is the author’s version of a work that was accepted for publication in Pattern Recognition. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition, 43, 36, (2010) DOI 10.1016/j.patcog.2009.09.018