A nature-inspired hybrid Fuzzy C-means algorithm for better clustering of biological data sets

Clustering is one of the widely used unsupervised methods to interpret and analyze huge amount of data in the field of Bioinformatics. One of the major issues involved in clustering is to address the growing data so that the cluster quality does not decrease with increase in the size of the data. In this work, we compare the promising clustering algorithms on various cancer domains and suggest improvements to them, with the help of a optimization techniques viz. Harmony Search (HS) algorithm. This paper discusses comparison of these techniques, various steps taken to achieve the target, and finally suggests an improved method that combines the merits of Fuzzy C-means algorithm and HS optimization technique.