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Erschienen in:
01.06.2013 | ICONIP 2011
PSO-based K-Means clustering with enhanced cluster matching for gene expression data
Erschienen in: Neural Computing and Applications | Ausgabe 7-8/2013
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Abstract
An integration of particle swarm optimization (PSO) and K-Means algorithm is becoming one of the popular strategies for solving clustering problem, especially unsupervised gene clustering. It is known as PSO-based K-Means clustering algorithm (PSO-KM). However, this approach causes the dimensionality of clustering problem to expand in PSO search space. The sequence of clusters represented in particle is not evaluated. This study proposes an enhanced cluster matching to further improve PSO-KM. In the proposed scheme, prior to the PSO updating process, the sequence of cluster centroids encoded in a particle is matched with the corresponding ones in the global best particle with the closest distance. On this basis, the sequence of centroids is evaluated and optimized with the closest distance. This makes particles to perform better in searching the optimum in collaborative manner. Experimental results show that this proposed scheme is more effective in reducing clustering error and improving convergence rate.