Gene expression data clustering using a multiobjective symmetry based clustering technique

Abstract

The invention of microarrays has rapidly changed the state of biological and biomedical research. Clustering algorithms play an important role in clustering microarray data sets where identifying groups of co-expressed genes are a very difficult task. Here we have posed the problem of clustering the microarray data as a multiobjective clustering problem. A new symmetry based fuzzy clustering technique is developed to solve this problem. The effectiveness of the proposed technique is demonstrated on five publicly available benchmark data sets. Results are compared with some widely used microarray clustering techniques. Statistical and biological significance tests have also been carried out.

Keywords: Archived multiobjective simulated annealing based technique (AMOSA); Automatic determination of number of clusters; Clustering; Gene expression data clustering; Microarray data; Multiobjective optimization (MOO); Symmetry.