A robust deterministic annealing algorithm for data clustering

Authors:
Xu-Lei Yang;Qing Song;Yi-Lei Wu
Affiliations:
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore;School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore;School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
Venue:
Data & Knowledge Engineering
Year:
2007

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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.