Nature inspired techniques for data clustering

Nature is always a source of inspiration. In last few decades, the research is stimulated on new computing paradigms and result of this effort is emergence of new problem solving techniques like Nature Inspired Computing, Evolutionary Computing. Nature inspired problem solving techniques are widely used to solve complex problems. These techniques are widely used due to their decentralized and self-organized behavior. Such behavior is observed in social systems such as artificial bee colony algorithm, particle swarm optimization, ant colony optimization, bat algorithm, firefly algorithm, glowworm swarm optimization etc. In this paper we have given overview of nature inspired techniques used for data clustering, hybridization with traditional clustering techniques and their effectiveness.