Flocking based approach for data clustering
Natural Computing: an international journal
Multistrategy self-organizing map learning for classification problems
Computational Intelligence and Neuroscience
RBF neural network based on particle swarm optimization
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
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Gene clustering, the process of grouping related genes in the same cluster, is at the foundation of different genomic studies that aim at analyzing the function of genes. Microarray technologies have made it possible to measure gene expression levels for thousands of genes simultaneously. For knowledge to be extracted from the datasets generated by these technologies, the datasets have to be presented to a scientist in a meaningful way. Gene clustering methods serve this purpose. In this paper, a hybrid clustering approach that is based on self-organizing maps and particle swarm optimization is proposed. In the proposed algorithm, the rate of convergence is improved by adding a conscience factor to the self-organizing maps algorithm. The robustness of the result is measured by using a resampling technique. The algorithm is implemented on a cluster of workstations. Copyright © 2004 John Wiley & Sons, Ltd.