A complete gradient clustering algorithm

Abstract

A gradient clustering algorithm, based on the nonparametric methodology of statistical kernel estimators, expanded to its complete form, enabling implementation without particular knowledge of the theoretical aspects or laborious research, is presented here. The possibilities of calculating tentative optimal parameter values, and then - based on illustrative interpretation - their potential changes, result in the proposed Complete Gradient Clustering Algorithm possessing many original and valuable, from an applicational point of view, properties. Above all the number of clusters is not arbitrarily imposed but fitted to a real data structure. It is also possible to increase the scale of the number (still avoiding arbitrary assumptions), as well as the proportion of clusters in areas of dense and sparse situation of data elements. The method is universal in character and can be applied to a wide range of practical problems, in particular from the bioinformatics, management and engineering fields. © 2011 Springer-Verlag.