How to cite this paper
Kumar, A., Kumar, D & Jarial, S. (2018). A novel hybrid K-means and artificial bee colony algorithm approach for data clustering.Decision Science Letters , 7(1), 65-76.
Refrences
Alatas, B. (2010). Chaotic bee colony algorithms for global numerical optimization. Expert Systems with Applications, 37(8), 5682-5687.
Al-Sultan, K.S. (1995). A tabu search approach to the clustering problem. Pattern Recognition, 28(9), 1443-1451.
Blickle, T., & Thiele, L. (1995). A mathematical analysis of tournament selection, in: Eshelman, L.(Ed.) Proceedings of Sixth International Conf. Genetic Algorithms (ICGA95), Morgan Kaufmann, San Francisco, CA, pp. 9-16.
Calinski, R., &Harabasz, J. (1974). A dendrite method for cluster analysis. Commun. Stat., 3(1), 1-27.
Chen, C.Y., & Ye, F. (2004). Particle swarm optimization algorithm and its application to clustering analysis. In IEEE International Conference on Networking, Sensing and Control, Taiwan, pp. 789-794.
Dalli, A. (2003). Adaptation of the F-measure to cluster-based Lexicon quality evaluation. In Proceedings of EACL 2003 Workshop, Budapest, pp. 51-56.
Davies, D. L., &Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, (2), 224-227.
Gan, G., Ma, C., & Wu, J. (2007). Data clustering: Theory, algorithms, and applications. ASA-SIAM Series on Statistics and Applied Probability, SIAM, Philadelphia, VA.
Gao, W., & Liu, S. (2012). A modified artificial bee colony algorithm. Computers & Operations Research, 39(3), 687-697.
Handl, J., Knowles, J., &Dorigo, M. (2003). On the performance of ant-based clustering. Design and Application of Hybrid Intelligent Systems: Frontiers in Artificial Intelligence and Applications,104, 204-213.
Hubert, L., &Arabie, P. (1985). Comparing Partitions. Journal of Classification, 2, 193-218.
Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization. Technical Report – TR06, Erciyes University.
Karaboga, D., &Ozturk, C. (2011). A novel clustering approach: Artificial bee colony (abc) algorithm. Applied Soft Computing, 11(1), 652-657.
Kumar, Y., &Sahoo, G. (2014). A charged system search approach for data clustering. Progress in Artificial Intelligence, 2, 153-166.
Miller, B.L., & Goldberg, D.E. (1995). Genetic algorithms, tournament selection, and the effects of noise. Complex Systems, 9, 193-212.
Murthy, C.A., & Chowdhury, N. (1996). In search of optimal clusters using genetic algorithms. Pattern Recognition Letters, 17(8), 825-832.
Rahnamayan, S., Tizhoosh, H.R., &Salama, M.M.A. (2008). Opposition-based differential evolution. IEEE Transactions on Evolutionary Computation,12(1), 64–79.
Santosa, B., &Mirsa, K.N. (2009). Cat swarm optimization for clustering. In International Conference on Soft Computing and Pattern Recognition (SOCPAR’09), pp. 54-59.
Satapathy, S.C., &Naik, A. (2011). Data clustering based on teaching-learning-based optimization. In Swarm, Evolutionary, and Memetic Computing, Springer Berlin Heidelberg, pp. 148-156.
Selim, S.Z., &Alsultan, K. (1991). A simulated annealing algorithm for the clustering problem. Pattern Recognition, 24(10), 1003-1008.
Shelokar, P.S., Jayaraman, V.K., & Kulkarni, B.D. (2004). An ant colony approach for clustering. AnalyticaChimicaActa, 509(2), 187-195.
Xu, R., Xu, J., &Wunsch II, D.C. (2012). A comparison study of validity indices on swarm-intelligence-based clustering. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 42(4), 1243-1256.
Al-Sultan, K.S. (1995). A tabu search approach to the clustering problem. Pattern Recognition, 28(9), 1443-1451.
Blickle, T., & Thiele, L. (1995). A mathematical analysis of tournament selection, in: Eshelman, L.(Ed.) Proceedings of Sixth International Conf. Genetic Algorithms (ICGA95), Morgan Kaufmann, San Francisco, CA, pp. 9-16.
Calinski, R., &Harabasz, J. (1974). A dendrite method for cluster analysis. Commun. Stat., 3(1), 1-27.
Chen, C.Y., & Ye, F. (2004). Particle swarm optimization algorithm and its application to clustering analysis. In IEEE International Conference on Networking, Sensing and Control, Taiwan, pp. 789-794.
Dalli, A. (2003). Adaptation of the F-measure to cluster-based Lexicon quality evaluation. In Proceedings of EACL 2003 Workshop, Budapest, pp. 51-56.
Davies, D. L., &Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, (2), 224-227.
Gan, G., Ma, C., & Wu, J. (2007). Data clustering: Theory, algorithms, and applications. ASA-SIAM Series on Statistics and Applied Probability, SIAM, Philadelphia, VA.
Gao, W., & Liu, S. (2012). A modified artificial bee colony algorithm. Computers & Operations Research, 39(3), 687-697.
Handl, J., Knowles, J., &Dorigo, M. (2003). On the performance of ant-based clustering. Design and Application of Hybrid Intelligent Systems: Frontiers in Artificial Intelligence and Applications,104, 204-213.
Hubert, L., &Arabie, P. (1985). Comparing Partitions. Journal of Classification, 2, 193-218.
Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization. Technical Report – TR06, Erciyes University.
Karaboga, D., &Ozturk, C. (2011). A novel clustering approach: Artificial bee colony (abc) algorithm. Applied Soft Computing, 11(1), 652-657.
Kumar, Y., &Sahoo, G. (2014). A charged system search approach for data clustering. Progress in Artificial Intelligence, 2, 153-166.
Miller, B.L., & Goldberg, D.E. (1995). Genetic algorithms, tournament selection, and the effects of noise. Complex Systems, 9, 193-212.
Murthy, C.A., & Chowdhury, N. (1996). In search of optimal clusters using genetic algorithms. Pattern Recognition Letters, 17(8), 825-832.
Rahnamayan, S., Tizhoosh, H.R., &Salama, M.M.A. (2008). Opposition-based differential evolution. IEEE Transactions on Evolutionary Computation,12(1), 64–79.
Santosa, B., &Mirsa, K.N. (2009). Cat swarm optimization for clustering. In International Conference on Soft Computing and Pattern Recognition (SOCPAR’09), pp. 54-59.
Satapathy, S.C., &Naik, A. (2011). Data clustering based on teaching-learning-based optimization. In Swarm, Evolutionary, and Memetic Computing, Springer Berlin Heidelberg, pp. 148-156.
Selim, S.Z., &Alsultan, K. (1991). A simulated annealing algorithm for the clustering problem. Pattern Recognition, 24(10), 1003-1008.
Shelokar, P.S., Jayaraman, V.K., & Kulkarni, B.D. (2004). An ant colony approach for clustering. AnalyticaChimicaActa, 509(2), 187-195.
Xu, R., Xu, J., &Wunsch II, D.C. (2012). A comparison study of validity indices on swarm-intelligence-based clustering. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 42(4), 1243-1256.