Clustering is one of the most important techniques, which group genes of similar expression pattern into a small number of meaningful homogeneous groups or clusters. Gene expression data has certain special characteristics and is a challenging research problem. There are many applications for clustering gene expression data. Clustering can be applied for genes called gene clustering. Hard clustering allows a gene to get placed in exactly one cluster and converges in local optima. Soft clustering approach allows gene to get placed in all the clusters with some membership values. As the hard clustering approach converges in local optimum, an evolutionary computation technique like swarm clustering is required to find the global optimum solution. This chapter studies swarm clustering techniques such as Particle Swarm Clustering K-Means, Cuckoo Search Clustering, Cuckoo Search Clustering with levy flight, harmony search, Fuzzy PSO and Ant Colony Optimization based Clustering for clustering gene expression data. Evaluation measures for clustering gene expression data are also discussed.
A novel harmony search-K means hybrid algorithm for clustering gene expression data
