A robust deterministic annealing algorithm for data clustering

Abstract

In this paper, a novel robust deterministic annealing (RDA) algorithm is developed for data clustering. This method takes advantage of conventional noise clustering (NC) and deterministic annealing (DA) algorithms in terms of the independence of data initialization, the ability to avoid poor local optima, the better performance for unbalanced data, and the robustness against noise and outliers. In addition, a cluster validity criterion, i.e., Vapnik–Chervonenkis (VC)-bound induced index, which is estimated based on the structural risk minimization (SRM) principle, is specifically extended for RDA to determine the optimal cluster number for a given data set. The superiority of the proposed RDA clustering algorithm is supported by experimental results.