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International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.11, No.6, Jun. 2019

A Comparative Analysis of Firefly and Fuzzy-Firefly based Kernelized Hybrid C-Means Algorithms

Full Text (PDF, 1929KB), PP.49-68


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Author(s)

B.K. Tripathy, Anmol Agrawal, A. Jayaram Reddy

Index Terms

Data Clustering;Image segmentation;Kernel function;Firefly;Fuzzy Firefly;DB Index;Dunn Index

Abstract

In most of the clustering algorithms, the assignment of initial centroids is performed randomly, which affects both the final outcome and the number of iterations required. Another aspect of the approaches in clustering algorithms is the use of Euclidean distance as the measure of similarity between data points, which is handicapped by linear separability of input data. The purpose of this paper is to combine suitable techniques so that both the above problems can be handled suitably leading to efficient algorithms. For the initial assignment of centroids we use Firefly and Fuzzy Firefly algorithms. We replace the Euclidean distance by Kernels (Gaussian and Hyper-tangent) leading to hybridized versions. For experimental analysis we use five different images from different domains as input. Two efficiency measures; Davis Bouldin index (DB) and Dunn index (D) are used for comparison. The tabular values, their graphical representations and output images are generated to support the claims. The analysis proves the superiority of the optimized algorithms over their existing counterparts. We also find that Hyper-tangent kernel with Rough Intuitionistic Fuzzy C-Means algorithm using Fuzzy Firefly algorithm produces the best results and has a much faster convergence rate. The analysis of medical, satellite or geographical images can be done more efficiently using the proposed optimized algorithms. It is supposed to play an important role in image segmentation and analysis.

Cite This Paper

B.K. Tripathy, Anmol Agrawal, A. Jayaram Reddy, "A Comparative Analysis of Firefly and Fuzzy-Firefly based Kernelized Hybrid C-Means Algorithms", International Journal of Intelligent Systems and Applications(IJISA), Vol.11, No.6, pp.49-68, 2019. DOI: 10.5815/ijisa.2019.06.05

Reference

[1]Zadeh, L.A., “Fuzzy Sets”, Information and Control, Vol. 8 No.3, 1965. pp.338 – 353.

[2]Bezdek, J. C., Ehrlich, R., and Full, W., “FCM: The fuzzy c-means clustering algorithm”, Computers & Geosciences, Vol. 10 No. 2-3, 1984, pp.191-203.

[3]Atanassov, K.T., “Intuitionistic Fuzzy Sets”, Fuzzy sets and Systems, Vol. 20 No.1, 1986, pp.87-96.

[4]Pawlak, Z., “Rough sets”, International Journal of Parallel Programming, Vol.11 No.5, 1982, pp.341-356

[5]Chaira, T., “A novel intuitionistic fuzzy C means clustering algorithm and its application to medical images”, Applied Soft Computing, Vol 11 No.2, 2011, pp.1711-1717.

[6]Maji, P. and Pal, S.K, “RFCM: A Hybrid Clustering Algorithm using rough and fuzzy set”, Fundamenta Informaticae, Vol. 8 No. 4, 2007, pp.475-496.

[7]Mitra, S., Banka, H.  and Pedrycz, W., “Rough-Fuzzy Collaborative Clustering”, IEEE Transactions on System, Man, and Cybernetics, Part B: Cybernetics, Vol. 36 No. 4, 2006, pp.795-805.

[8]Bhargava, R. Tripathy, B. K., Tripathy, A., Dhull, R., Verma, E. and Swarnalatha, P., “Rough intuitionistic fuzzy C-means algorithm and a comparative analysis”, Proceedings of ACM Compute-2013, International Conference, SITE, VIT University, 21-22 August.

[9]Zhang, D. and Chen, S., “Fuzzy clustering using kernel method”, in International conference on Control and Automation, Xiamen, China, 2002, pp.123-127.

[10]Tripathy, B. K., Ghosh, A., and Panda, G. K., “Kernel Based K-Means Clustering Using Rough Set”, in Proceedings of 2012 International Conference on Computer Communication and Informatics (ICCCI -2012), Jan. 10 – 12, Coimbatore, INDIA, pp.1 -5.

[11]Tripathy, B.K., and Bhargav, R., “Kernel Based Rough-Fuzzy C-Means”, in International Conference on Pattern Recognition and Machine Intelligence (PReMI), ISI Calcutta, December 2013, LNCS 8251, pp.148-157

[12]Tripathy, B.K., Tripathy, A., Govindarajulu, K. and Bhargav, R., “On Kernel Based Rough Intuitionistic Fuzzy C-means Algorithm and a Comparative Analysis” Smart Innovation, Systems and Technologies, 2014.

[13]Tripathy, B.K., and Mittal, D.,  “Efficiency Analysis of Kernel Functions in Uncertainty Based C-Means Algorithms”, International Conference on Advances in Computing, Communications and Informatics, ICACCI 2015, Article number 7275709,  pp. 807-813.

[14]Tyshchenko, Oleksii, Bodyanskiy, Yevgeniy, Hu, Zhengbing and Samitova, Viktoriia. “Fuzzy Clustering Data Given in the Ordinal Scale”, in International Journal of Intelligent Systems and Applications, 2017, Vol 9. No. 1, pp. 67-74.

[15]Yang, Xin-She (2009), “Firefly algorithms for multimodal optimization”, O. Watanabe and T. Zeugmann (Eds.): SAGA 2009, Vol. 5792, pp. 169–178.

[16]Hassanzadeh, T. and Kanan, H. R., “Fuzzy FA: A modified firefly algorithm”, Applied Artificial Intelligence. Vol 28 No. 1, 2014, pp. 47-65.

[17]Jain, A., Chinta, S. and Tripathy, B.K., “Stabilizing Rough Sets Based Clustering Algorithms Using Firefly Algorithm over Image Datasets”, in 2nd International Conference on Information and Communication Technology for Intelligent Systems (ICTIS 2017),  2017, pp.325-332.

[18]Chinta, S., Jain, A. and Tripathy, B. K., “Image Segmentation Using Hybridized Firefly Algorithm and Intuitionistic Fuzzy C-Means”, in 1st International Conference On Smart Systems ,Innovations and Computing, Manipal University, Jaipur, 2018.

[19]Davis, D.L. and Bouldin, D.W., “A cluster separation measure”, IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI Vol 1 No.2, 1979, pp.224 – 227.

[20]Dunn, J. C., “A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters”, Journal of Cybernetics, Vol 3 No. 3, 1974, pp.32-57.

[21]Jain, A. K., Murty, M. N. and Flynn, P. J., “Data clustering: a review”, ACM Computing Surveys, Vol. 31 No. 3, 1999, pp.264-323.