Title: A novel chemical reaction-based clustering and its performance analysis

Authors: Archana Baral; H.S. Behera

Addresses: Department of Computer Science and Engineering, Veer Surendra Sai University of Technology (VSSUT), Burla, 768018, India ' Department of Computer Science and Engineering, Veer Surendra Sai University of Technology (VSSUT), Burla, 768018, India

Abstract: Clustering is widely used in case of pattern recognition, decision-making, machine-learning, image processing and many more real world problems. Many algorithms have been developed for better clustering. This paper proposes an efficient way of clustering data using chemical reaction optimisation (CRO), a recently developed metaheuristic for solving optimisation problems. By taking into consideration some of the real world datasets, the performance of the proposed algorithm has been compared with K-means, genetic algorithm (GA), differential evolution (DE) and teaching-learning-based optimisation (TLBO). Experimental result shows that the performance of CRO-based clustering is better than K-means, GA, DE and TLBO, in terms of quantisation error, intra and inter cluster distance, etc. It is also observed that the proposed CRO-clustering algorithm converges remarkably faster in comparison to other algorithms.

Keywords: K-means clustering; genetic algorithms; differential evolution; teaching-learning based optimisation; chemical reaction optimisation; clustering algorithms; metaheuristics.

DOI: 10.1504/IJBIDM.2013.057743

International Journal of Business Intelligence and Data Mining, 2013 Vol.8 No.2, pp.184 - 198

Received: 06 Jun 2013
Accepted: 11 Jul 2013
Published online: 28 Jun 2014 *

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