Gene Clustering Using Particle Swarm Optimizer Based Memetic Algorithm

K-means is one of the most commonly used clustering methods for analyzing gene expression data, where it is sensitive to the choice of initial clustering centroids and tends to be trapped in local optima. To overcome these problems, a memetic K-means (MKMA) algorithm, which is a hybridization of particle swarm optimizer (PSO) based memetic algorithm (MA) and K-means, is proposed in this paper. In particular, the PSO based MA is used to minimize the within-cluster sum of squares and the K-means is used to iteratively fine-tune the locations of the centers. The experimental results on two gene expression datasets indicate that MKMA is capable of obtaining more compact clusters than K-means, Fuzzy K-means, and the other PSO based K-means namely PK-means. MKMA is also demonstrated to attain faster convergence rate and more robustness against the random choice of initial centroids.