Research Journal of Applied Sciences

Year: 2014
Volume: 9
Issue: 7
Page No. 452 - 460

Differential Evolution for Fuzzy Clustering Using Self-Adaptive Trade-Off Between Exploitation and Exploration

Authors : Siriporn Supratid and Phichete Julrode

References

Alpaydm, E., 2004. Introduction to Machine Learning. The MIT Press, Cambridge, MA., USA., ISBN-13: 9780262012119, pp: 1-3.

Aydin, D., 2007. Comparison of regression models based on nonparametric estimation techniques: Prediction of GDP in Turkey. Int. J. Math. Models Methods Applied Sci., 1: 70-75.
Direct Link  |  

Bezdek, J.C., 1981. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York, USA., ISBN-13: 978-1475704525, Pages: 256.

Civicioglu, P. and E. Besdok, 2013. A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms. Artif. Intell. Rev., 39: 315-346.
CrossRef  |  Direct Link  |  

Dalli, A., 2003. Adaptation of the F-measure to cluster-based Lexicon quality evaluation. Proceedings of the 10th Conference of the European Chapter of the Association for Computational Linguistics, April 12-17, 2003, Agro Hotel, Budapest, Hungary, pp: 51-56.

Dogan, B. and M. Korurek, 2012. A new ECG beat clustering method based on kernelized fuzzy c-means and hybrid ant colony optimization for continuous domains. Applied Soft Comput., 12: 3442-3451.
CrossRef  |  Direct Link  |  

Dunn, J.C., 1973. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J. Cybernet., 3: 32-57.
CrossRef  |  Direct Link  |  

Han, M.F., C.T. Lin and J.Y. Chang, 2013. Efficient differential evolution algorithm-based optimisation of fuzzy prediction model for time series forecasting. Int. J. Intell. Inform. Database Syst., 7: 225-241.
CrossRef  |  Direct Link  |  

Handl, J., J. Knowles and M. Dorigo, 2003. On the performance of ant-based clustering. Proceedings of the 3rd International Conference on Hybrid Intelligent Systems, December 14-17, 2003, Melbourne, Australia, pp: 204-213.

Hastie, T., R. Tibshirani and J. Friedman, 2009. Hierarchical Clustering. In: The Elements of Statistical Learning: Data Mining, Inference and Prediction, Hastie, T., R. Tibshirani and J. Friedman (Eds.). 2nd Edn., Springer, New York, USA., ISBN-13: 9780387848587, pp: 520-528.

Herrero, A., E. Corchado and J. Alfredo, 2011. Unsupervised neural models for country and political risk analysis. Expert Syst. Appl., 38: 13641-13661.
CrossRef  |  

Izakian, H. and A. Abraham, 2011. Fuzzy C-means and fuzzy swarm for fuzzy clustering problem. Expert Syst. Appl., 38: 1835-1838.
CrossRef  |  Direct Link  |  

Kao, Y. and C.C. Chen, 2013. A differential evolution fuzzy clustering approach to machine cell formation. Int. J. Adv. Manuf. Technol., 65: 1247-1259.
CrossRef  |  Direct Link  |  

Kohonen, T., 1995. Self-Organizing Maps. 1st Edn., Springer, New York, USA.

Li, X., C. Hu and X. Yan, 2013. Chaotic differential evolution algorithm based on competitive coevolution and its application to dynamic optimization of chemical processes. Intell. Autom. Soft Comput., 19: 85-98.
CrossRef  |  Direct Link  |  

Memmedli, M. and A. Nizamitdinov, 2013. An application of various nonparametric techniques by nonparametric regression splines. Int. J. Math. Models Methods Applied Sci., 6: 106-113.
Direct Link  |  

Montgomery, J., M. Randall and A. Lewis, 2011. Differential evolution for RFID antenna design: A comparison with ant colony optimisation. Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, July 12-16, 2011, Dublin, Ireland, pp: 673-680.

Olson, C.F., 1995. Parallel algorithms for hierarchical clustering. Parallel Comput., 21: 1313-1325.
CrossRef  |  

Pai, D.R., K.D. Lawrence, R.K. Klimberg and S.M. Lawrence, 2012. Experimental comparison of parametric, non-parametric and hybrid multigroup classification. Expert Syst. Appl., 39: 8593-8603.
CrossRef  |  

Ravi, V., N. Aggarwal and N. Chauhan, 2010. Differential evolution based fuzzy clustering. Proceedings of the 1st International Conference on Swarm, Evolutionary and Memetic Computing, December 16-18, 2010, Chennai, India, pp: 38-45.

Rojas, R., 1996. Neural Networks: A Systematic Introduction. Springer-Verlag, Berlin, Germany, ISBN-13: 9783540605058, Pages: 502.

Sayah, S., A. Hamouda and K. Zehar, 2013. Economic dispatch using improved differential evolution approach: A case study of the Algerian electrical network. Arabian J. Sci. Eng., 38: 715-722.
CrossRef  |  Direct Link  |  

Slowik, A., 2011. Fuzzy control of trade-off between exploration and exploitation properties of evolutionary algorithms. Proceedings of the 6th International Conference on Hybrid Artificial Intelligent Systems, May 23-25, 2011, Wroclaw, Poland, pp: 59-66.

Storn, R. and K. Price, 1997. Differential evolution-A simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim., 11: 341-359.
CrossRef  |  Direct Link  |  

Tan, P., M. Steinbach and V. Kumar, 2004. Introduction to Data Mining. Addison-Wesley, USA., pp: 1-35.

Tin, H.W., S.W. Leu and S.H. Chang, 2012. An PSO-based approach to speed up the fractal encoding. Int. J. Math. Models Methods Applied Sci., 6: 499-506.
Direct Link  |  

Vucetic, D. and S.P. Simonovic, 2013. Evaluation and application of fuzzy differential evolution approach for benchmark optimization and reservoir operation problems. J. Hydroinform., 15: 1456-1473.

Wang, C.X., C.H. Li, H. Dong and F. Zhang, 2013. An efficient differential evolution algorithm for function optimization. Inform. Technol. J., 12: 444-448.
CrossRef  |  Direct Link  |  

Wang, X. and S. Zhao, 2013. Differential evolution algorithm with self-adaptive population resizing mechanism. Math. Problems Eng. 10.1155/2013/419372

Webb, A.R., 2002. Statistical Pattern Recognition. 2nd Edn., John Wiley and Sons Ltd., UK., ISBN-13: 978-0470845134, pp: 1-31.

Xie, X.L. and G. Beni, 1991. A validity Measure for fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell., 13: 841-847.
CrossRef  |  Direct Link  |  

Design and power by Medwell Web Development Team. © Medwell Publishing 2022 All Rights Reserved