Title: A novel hybrid approach for real world data clustering algorithm based on fuzzy C-means and firefly algorithm

Authors: Himansu Sekhar Behera; Janmenjoy Nayak; M. Nanda; K. Nayak

Addresses: Department of Computer Science Engineering and Information Technology, Veer Surendra Sai University of Technology, Burla – 768018, Odisha, India ' Department of Computer Science Engineering and Information Technology, Veer Surendra Sai University of Technology, Burla – 768018, Odisha, India ' Department of Computer Science Engineering and Information Technology, Veer Surendra Sai University of Technology, Burla – 768018, Odisha, India ' Department of Computer Science Engineering and Information Technology, Veer Surendra Sai University of Technology, Burla – 768018, Odisha, India

Abstract: Fuzzy clustering plays an important role in the current research area for several real world applications. But as it is highly sensitive to initialisations and suffers from local optima, it is not being well suited for many real world problems. Firefly algorithm is a nature inspired metaheuristic optimisation algorithm which is inspired by the simulation of flashing behaviour of fireflies. In order to overcome the shortcomings of fuzzy C-means (FCM) algorithm, a hybridised fuzzy clustering algorithm has been proposed by combining the fuzzy C-means with firefly (FCM-FA) algorithm by extracting the positive insights of both the algorithm for faster convergence than FCM. The performance of proposed hybridised FCM-FA has been compared with other used algorithms for various real world datasets. The experimental results show that the hybrid FCM-FA is better and more effective clustering method for real world applications as compared to other existing algorithms.

Keywords: particle swarm optimisation; PSO; fuzzy C-means; FCM; firefly algorithm; data clustering; fuzzy clustering.

DOI: 10.1504/IJFCM.2015.076274

International Journal of Fuzzy Computation and Modelling, 2015 Vol.1 No.4, pp.431 - 448

Available online: 29 Apr 2016 *

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