Gene Expression Classification Using a Fuzzy Point Symmetry Based PSO Clustering Technique

The growth of biomedical and biological research has changed the shape after introduction of microarray technology. Several unsupervised clustering techniques have been introduced in order to explain and interpret the microarray gene expression data sets. A new clustering technique using fuzzy point symmetric concept has been proposed which utilizes particle swarm optimization as the underline optimization strategy. This paper has deployed the clustering of microarray data as a single objective optimization problem. The efficacy of the proposed fuzzy clustering technique which poses the symmetric property is compared with some well known clustering algorithms utilizing the properties of symmetry and genetic algorithms over some gene-microarray datasets which are publicly available. Biological and statistical analysis have been carried out to validate the obtained clustering results.