A new clustering algorithm based on PSO with the jumping mechanism of SA
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Gene clustering using particle swarm optimizer based memetic algorithm
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
Engineering Applications of Artificial Intelligence
eXploratory K-Means: A new simple and efficient algorithm for gene clustering
Applied Soft Computing
Improved gene clustering based on particle swarm optimization, k-means, and cluster matching
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Review article: Computational intelligence techniques in bioinformatics
Computational Biology and Chemistry
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Microarray technology has been widely applied in study of measuring gene expression levels for thousands of genes simultaneously. Gene cluster analysis is found useful for discovering the function of gene because co-expressed genes are likely to share the same biological function. K-means is one of well-known clustering methods. However, it is sensitive to the selection of an initial clustering and easily becoming trapped in a local minimum. Particle-pair optimizer (PPO) is a variation on the traditional particle swarm optimization (PSO) algorithm, which is stochastic particle-pair based optimization technique that can be applied to a wide range of problems. In this paper we bridges PPO and K-means within the algorithm PK-means for the first time. Our results indicate that PK-means clustering is generally more accurate than K-means and Fuzzy K-means (FKM). PK-means also has better robustness for it is less sensitive to the initial randomly selected cluster centroids. Finally, our algorithm outperforms these methods with fast convergence rate and low computation load.