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How to cite this paper
APA: Hosseini, M., Sadeghzade, M & Nourmandi-Pour, R. (2014). An efficient approach based on differential evolution algorithm for data clustering. Decision Science Letters, 3(3), 319-324.
Chicago/Turabian: Hosseini, M., Sadeghzade, M & Nourmandi-Pour, R. 2014. "An efficient approach based on differential evolution algorithm for data clustering." Decision Science Letters 3, no. 3 (2014): 319-324.
AMA: Hosseini, M., Sadeghzade, M & Nourmandi-Pour, R. An efficient approach based on differential evolution algorithm for data clustering. Decision Science Letters. 2014;3(3):319-324.
References
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Bin, W., Yi, Z., Shaohui, L., & Zhongzhi, S. (2002, May). CSIM: a document clustering algorithm based on swarm intelligence. In Evolutionary Computation, 2002. CEC & apos; 02. Proceedings of the 2002 Congress on (Vol. 1, pp. 477-482). IEEE.
B?ck, T., & Schwefel, H. P. (1993). An overview of evolutionary algorithms for parameter optimization. Evolutionary computation, 1(1), 1-23.
Eiben, A. E., & Smith, J. E. (2003). Introduction to evolutionary computing. springer.
Frigui, H., & Krishnapuram, R. (1999). A robust competitive clustering algorithm with applications in computer vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(5), 450-465.
Gong, W., Cai, Z., & Jiang, L. (2008). Enhancing the performance of differential evolution using orthogonal design method. Applied Mathematics and Computation, 206(1), 56-69.
Han, J., Kamber, M., & Pei, J. (2006). Data mining: concepts and techniques. Morgan kaufmann.
Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: a review. ACM computing surveys (CSUR), 31(3), 264-323.
Jiawei, H., & Kamber, M. (2001). Data mining: concepts and techniques. San Francisco, CA, itd: Morgan Kaufmann, 5.
Leung, Y., Zhang, J. S., & Xu, Z. B. (2000). Clustering by scale-space filtering.Pattern Analysis and Machine Intelligence, IEEE Transactions on, 22(12), 1396-1410.
Liu, Y., Wu, X., & Shen, Y. (2011). Automatic clustering using genetic algorithms. Applied Mathematics and Computation, 218(4), 1267-1279.
Shelokar, P. S., Jayaraman, V. K., & Kulkarni, B. D. (2004). An ant colony approach for clustering. Analytica Chimica Acta, 509(2), 187-195.
Mirkin, B. (1998). Mathematical classification and clustering: From how to what and why (pp. 172-181). Springer Berlin Heidelberg.
Noman, N., & Iba, H. (2008). Accelerating differential evolution using an adaptive local search. Evolutionary Computation, IEEE Transactions on, 12(1), 107-125.
Rokach, L., & Maimon, O. (2005). Clustering methods. In Data mining and knowledge discovery handbook (pp. 321-352). Springer US.
Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11(4), 341-359.
Qian, W. (2008). Adaptive differential evolution algorithm for multiobjective optimization problems. Applied Mathematics and Computation, 201(1), 431-440.
Yang, X. S. (2009). Firefly algorithms for multimodal optimization. In Stochastic algorithms: foundations and applications (pp. 169-178). Springer Berlin Heidelberg.
Bin, W., Yi, Z., Shaohui, L., & Zhongzhi, S. (2002, May). CSIM: a document clustering algorithm based on swarm intelligence. In Evolutionary Computation, 2002. CEC & apos; 02. Proceedings of the 2002 Congress on (Vol. 1, pp. 477-482). IEEE.
B?ck, T., & Schwefel, H. P. (1993). An overview of evolutionary algorithms for parameter optimization. Evolutionary computation, 1(1), 1-23.
Eiben, A. E., & Smith, J. E. (2003). Introduction to evolutionary computing. springer.
Frigui, H., & Krishnapuram, R. (1999). A robust competitive clustering algorithm with applications in computer vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(5), 450-465.
Gong, W., Cai, Z., & Jiang, L. (2008). Enhancing the performance of differential evolution using orthogonal design method. Applied Mathematics and Computation, 206(1), 56-69.
Han, J., Kamber, M., & Pei, J. (2006). Data mining: concepts and techniques. Morgan kaufmann.
Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: a review. ACM computing surveys (CSUR), 31(3), 264-323.
Jiawei, H., & Kamber, M. (2001). Data mining: concepts and techniques. San Francisco, CA, itd: Morgan Kaufmann, 5.
Leung, Y., Zhang, J. S., & Xu, Z. B. (2000). Clustering by scale-space filtering.Pattern Analysis and Machine Intelligence, IEEE Transactions on, 22(12), 1396-1410.
Liu, Y., Wu, X., & Shen, Y. (2011). Automatic clustering using genetic algorithms. Applied Mathematics and Computation, 218(4), 1267-1279.
Shelokar, P. S., Jayaraman, V. K., & Kulkarni, B. D. (2004). An ant colony approach for clustering. Analytica Chimica Acta, 509(2), 187-195.
Mirkin, B. (1998). Mathematical classification and clustering: From how to what and why (pp. 172-181). Springer Berlin Heidelberg.
Noman, N., & Iba, H. (2008). Accelerating differential evolution using an adaptive local search. Evolutionary Computation, IEEE Transactions on, 12(1), 107-125.
Rokach, L., & Maimon, O. (2005). Clustering methods. In Data mining and knowledge discovery handbook (pp. 321-352). Springer US.
Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11(4), 341-359.
Qian, W. (2008). Adaptive differential evolution algorithm for multiobjective optimization problems. Applied Mathematics and Computation, 201(1), 431-440.
Yang, X. S. (2009). Firefly algorithms for multimodal optimization. In Stochastic algorithms: foundations and applications (pp. 169-178). Springer Berlin Heidelberg.