Applications and Advancements of Nature-Inspired Optimization Algorithms in Data Clustering: A Detailed Analysis

Abstract

In the last decade, nature-inspired optimization algorithm has been a keen interest among the researchers of optimization community. Most of nature-inspired algorithms are developed through the simulating behavior of natural agents in nature. In comparison with evolutionary- and swarm-based algorithms, these are most effective techniques for all real-life applications. Although both swarm- and evolutionary-based algorithms are one of the subsets of nature-inspired optimization algorithm but the efficiency and effectiveness of such algorithm make them more attractive to use in various data mining problems. Among the other tasks of data mining, it has been always a challenging task to solve clustering problem, which is unsupervised in nature. In this paper, a brief study has been conducted on the applications of nature-inspired optimization algorithms in clustering techniques. Also, few challenging issues along with the advancements of various nature-inspired optimization algorithms are realized in the field of clustering.