Modified adaptive inertia weight particle swarm optimisation for data clustering. (7th March 2022)

• Record Type: Journal Article
• Authors: Yadav, Vikash; Gupta, Indresh Kumar
• Is Part Of: International journal of innovative computing and applications. Volume 13:Number 1(2022)
• Page Start: 34
• Page End: 40
• Publication Date: 2022-03-07
• Subjects: data clustering -- K-means clustering -- particle swarms optimisation -- PSO -- fitness function; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

Data clustering is widely applied in many real world domain including marketing, anthropology, medical science, engineering, economics, and others. It concerns with the partition of unlabelled dataset objects into clusters (groups) based on a similarity measure. The partitioning approach of dataset objects must follow that intra-cluster distances are smaller and inter-cluster distances are larger. In the current work, particle swarm optimisation (PSO) is employed for clustering. Some times the PSO may get stuck into a local optima; to overcome the PSO algorithm's trapping in a local optima a modified adaptive inertia weight particle swarm optimisation (MAIWPSO) is developed for data clustering based on fitness value of particles. K-means, PSO and MAIWPSO for clustering have been simulated on six standard dataset namely iris, thyroid, heart, breast cancer, crude oil and pima. Simulation results confirm MAIWPSO is a better approach for clustering against K-means and PSO.