Document Type

Conference Paper

Department/Unit

Department of Computer Science

Title

A cooperative and penalized competitive learning approach to gaussian mixture clustering

Language

English

Abstract

Competitive learning approaches with penalization or cooperation mechanism have been applied to unsupervised data clustering due to their attractive ability of automatic cluster number selection. In this paper, we further investigate the properties of different competitive strategies and propose a novel learning algorithm called Cooperative and Penalized Competitive Learning (CPCL), which implements the cooperation and penalization mechanisms simultaneously in a single competitive learning process. The integration of these two different kinds of competition mechanisms enables the CPCL to have good convergence speed, precision and robustness. Experiments on Gaussian mixture clustering are performed to investigate the proposed algorithm. The promising results demonstrate its superiority. © 2010 Springer-Verlag Berlin Heidelberg.

Publication Date

2010

Source Publication Title

Artificial Neural Networks – ICANN 2010: 20th International Conference, Thessaloniki, Greece, September 15-18, 2010, Proceedings, Part III

Start Page

435

End Page

440

Conference Location

Thessaloniki, Greece

Publisher

Springer

DOI

10.1007/978-3-642-15825-4_58

ISBN (print)

9783642158247

ISBN (electronic)

9783642158254

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