Survey on Clustering Problems Using Metaheuristic Algorithms

Data clustering is a highly sought-after approach to classify data objects. Different conventional partitioning and hierarchical clustering approaches are not capable of handling abstract and diversified real-world data generated in today's time. Clustering is an optimization and NP-hard problem. The research in the last decade shows that metaheuristics are successful and reliable techniques to generate optimized solutions for problems having infinite search space. This paper explores the scope, feasibility, and related work of metaheuristics to solve the clustering problem. Keywords: Clustering, Partial Swarm, Neighbourhood search.