A novel chemical reaction-based clustering and its performance analysis
Archana Baral, Himansu Sekhar Behera · International Journal of Business Intelligence and Data Mining · 2013
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 metaheuristics 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.