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Computational Intelligence and Neuroscience
Volume 2011 (2011), Article ID 121787, 11 pages
http://dx.doi.org/10.1155/2011/121787
Research Article

Multistrategy Self-Organizing Map Learning for Classification Problems

Soft Computing Research Group, Faculty of Computer Science and Information System, Universiti Teknologi Malaysia, Skudai, 81300 Johor, Malaysia

Received 12 January 2011; Revised 21 April 2011; Accepted 23 June 2011

Academic Editor: Francois Benoit Vialatte

Copyright © 2011 S. Hasan and S. M. Shamsuddin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Multistrategy Learning of Self-Organizing Map (SOM) and Particle Swarm Optimization (PSO) is commonly implemented in clustering domain due to its capabilities in handling complex data characteristics. However, some of these multistrategy learning architectures have weaknesses such as slow convergence time always being trapped in the local minima. This paper proposes multistrategy learning of SOM lattice structure with Particle Swarm Optimisation which is called ESOMPSO for solving various classification problems. The enhancement of SOM lattice structure is implemented by introducing a new hexagon formulation for better mapping quality in data classification and labeling. The weights of the enhanced SOM are optimised using PSO to obtain better output quality. The proposed method has been tested on various standard datasets with substantial comparisons with existing SOM network and various distance measurement. The results show that our proposed method yields a promising result with better average accuracy and quantisation errors compared to the other methods as well as convincing significant test.