Reliable clustering indexes

Abstract

This paper deals with a major challenge in clustering that is optimal model selection. It presents new efficient clustering quality indexes relying on feature maximization, which is an alternative measure to usual distributional measures relying on entropy or on Chi-square metric or vector-based measures such as Euclidean distance or correlation distance. Experiments compare the behavior of these new indexes with usual cluster quality indexes based on Euclidean distance on different kinds of test datasets for which ground truth is available. This comparison clearly highlights altogether the superior accuracy and stability of the new method, its efficiency from low to high dimensional range and its tolerance to noise.