References: [1] Huang, Xiaohui., Ye, Yunming., Zhang, Haijun . (2014). Extensions of Kmeans-Type Algorithms: A New Clustering Framework by Integrating Intracluster Compactness and Intercluster Separation, IEEE Transactions on Neural Networks and Learning systems, 25 (8) 1433-1446. [2] Binu, D. (2015). Cluster analysis using optimization algorithms with newly designed objective functions, Expert Systems with Applications, 42 (14) 5848–5859. [3] Weiguo Sheng., Shengyong Chen., Michael Fairhurst.,Gang Xiao., and Jiafa Mao. (2014). Multilocal Search and Adaptive Niching Based Memetic Algorithm With a Consensus Criterion for Data Clustering, IEEE Transactions on Eevolutionary Computation, 18 (5) 721-741, OCTOBER 2014. [4] Josef Tvrdík., Ivan Krivy. (2015). Hybrid differential evolution algorithm for optimal clustering, Applied Soft Computing, 35, p. 502–512. [5] Kuo, R.J., Huang, Y.D., Lin, Chih-Chieh., Wu, Yung-Hung., Zulvia, Ferani E.. (2014). Automatic Kernel Clustering with Bee Colony Optimization Algorithm, Information Sciences, 283, p.107–122. [6] Yuwono, Mitchell., Su, Steven W., Moulton, Bruce D., Nguyen, Hung T. . (2014). Data Clustering Using Variants of Rapid Centroid Estimation, IEEE Transactions on Evolutionary Computation, 18 (3) 366-377. [7] Jonathon K. Parker., and Lawrence O. Hall. (2014). Accelerating Fuzzy-C Means Using an Estimated Subsample Size, IEEE transactions on fuzzy systems, 22 (5) 1229-12445. [8] Silva Filho, Telmo M., Pimentel, Bruno A., Souza, Renata M.C.R., Oliveira, Adriano L.I. (2015). Hybrid methods for fuzzy clustering based on fuzzy c-means and improved particle swarm optimization, Expert Systems with Applications, 42 (17-18) 6315– 6328. [9] Xu, R., Wunsch, D. I. (2005). Survey of clustering algorithms, IEEE Transactions on Neural Networks, 16 (3) 645–678. [10] Pal, N., Pal, K., Keller, J., Bezdek, J. (2005). A possibilistic fuzzy c-means clustering algorithm, IEEE Transactions on Fuzzy Systems, 13 (4) 517–530. [11] Jain, A. K. (2010). Data clustering: 50 years beyondk-means, Pattern Recognit. Lett., 31 (8) 651–666. [12] Mualik, U., Bandyopadhyay, S. (2002). Genetic algorithm based clustering technique, Pattern Recognition, 33, 1455–1465, 2002. [13] Premalatha, K., Natarajan, A. M. (2008). A new approach for data clustering based on PSO with local search, Computer and Information Science, 1 (4) 2008. [14] Zhang, C., Ouyang, D., Ning, J. (2010). An artificial bee colony approach for clustering, Expert Systems with Applications, 37, 4761–4767. [15] Wan, M., Li, L., Xiao, J., Wang, C., Yang, Y. (2012). Data clustering using bacterial foraging optimization, Journal of Intelligent Information Systems, 38 (2) 321–341. [16] Das, S., Abraham, A., Konar, A. (2008). Automatic clustering using an improved differential evolution algorithm, IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans, 38 (1) 218–237. [17] Castellanos-Garzón, J. A., Diaz, F. (2013). An evolutionary computational model applied to cluster analysis of DNA microarray data, Expert Systems with Applications, 40 (7) 2575–2591, 2013. [18] Senthilnath, J., Omkar, S. N., Mani, V. (2011). Clustering using firefly algorithm: Performance stud, Swarm and Evolutionary Computation, V. 1, p. 164–171. [19] Kuo, R. J., Syu, Y. J., Chen, Z. Y., Tien, F. C. (2012). Integration of particle swarm optimization and genetic algorithm for dynamic clustering, Journal of Information Sciences, 19, 124–140. [20] Selim, S. Z., Alsultan, K. (1991). A simulated annealing algorithm for the clustering problem, Pattern Recognition, 10 (24) 1003–1008. [21] Pavel Berkhin. (2002). Survey Of Clustering Data Mining Techniques, 2002. [22] Yasodha, M., Mohanraj, M. (2011). Clustering Algorithms for Biological Data - A Survey Approach, CiiT journal of data mining and knowledge engineering, 3 (3). [23] Datasets from “http://archive.ics.uci.edu/ml/”. [24] Mirjalili, Seyedali., Mirjalili, Seyed Mohammad., Lewis, Andrew (2014). Grey Wolf Optimizer, Advances in Engineering Software, 69, p 46–61. |