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A Hybrid Optimization Algorithm Based on Cuckoo Search and PSO for Data Clustering
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Abstract
Data clustering as one of the important data mining techniques is a fundamental and widely used method to achieve useful information about data. The purpose of clustering is to group together data points, which are close to one another. In face of the clustering problem, clustering methods still suffer from trapping in a local optimum and cannot often find global clusters. In order to overcome the shortcoming of the available clustering methods, this paper presents a hybrid clustering algorithm. This paper is presented an efficient hybrid evolutionary optimization algorithm based on combining Particle Swarm Optimization (PSO) and Cuckoo Search (CS), called CS-PSO, for optimally clustering N object into K clusters. The new CS-PSO algorithm is tested on several data sets, and its performance is compared with those of GA, FCM, Fuzzy-PSO and K-means clustering. The simulation results show that the new method carries out better results than the Genetic algorithm (GA), K-means, Fuzzy C-means FCM), Genetic-K means and Fuzzy-PSO.
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References
A. K. Jain, and R. C. Dubes, “Algorithms for Clustering data”, Pentice-Hill, 1988.
Hesam Izakian, Ajith Abraham, Vaclav Snasel, "Fuzzy Clustering Using Hybrid Fuzzy c-means and Fuzzy Particle Swarm Optimization", World Congress on Nature and Biologically Inspired Computing (NaBIC 2009), India, IEEE Press, pp. 1690-1694, 2009.
Swagatam Das, Ajith Abraham, Amit Konar, "Automatic Clustering Using an Improved Differential Evolution Algorithm", IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems And Humans, Vol. 38, No. 1, 2008.
E. W. Forgy, “Cluster analysis of multivariate data: Efficiency versus interpretability of classification,” Biometrics, Vol. 21, No. 3, pp. 768–769, 1965.
C. T. Zahn, “Graph-theoretical methods for detecting and describing gestalt clusters,” IEEE Trans. Comput., Vol. C-20, No. 1, pp. 68–86, Jan. 1971.
T. Mitchell, Machine Learning. New York: McGraw-Hill, 1997.
J. Mao and A. K. Jain, “Artificial neural networks for feature extraction and multivariate data projection,” IEEE Trans. Neural Netw., Vol. 6, No. 2, pp. 296–317, 1995.
E. Falkenauer, “Genetic Algorithms and Grouping Problems”, Chichester, U.K.: Wiley, 1998.
Sandra Paterlini, Thiemo Krink, "High Performance Clustering with Differential Evolution", IEEE Congress on Evolutionary Computation, CEC 2004, Vol. 2. pp: 2004-2011, 2004.
Holland J.H., “Adaptation in Natural and Artificial Systems”, University of Michigan Press, Ann Harbor, 1975.
Mohammad Al Hasan, Vineet Chaoji, Saeed Salem, and Mohammed J. Zaki, "Robust Partitional Clustering by Outlier and Density Insensitive Seeding", Pattern Recognition Letters, Vol: 30, No: 11, pp: 994-1002, 2009.
Taher Niknam, Bahman Bahmani Firouzi and Majid Nayeripour, "An Efficient Hybrid Evolutionary Algorithm for Cluster Analysis", World Applied Sciences Journal, Vol: 4, No: 2, pp: 300-307, 2008.
G. P. Babu, “Connectionist and evolutionary approaches for pattern clustering,” Dept. Comput. Sci. Automat., Indian Inst. Sci., Bangalore, Apr. 1994.
R. W. Klein and R. C. Dubes, “Experiments in projection and clustering by simulated annealing,” Pattern Recognit., vol. 22, pp. 213–220, 1989.
S. Z. Selim and K. Alsultan, “A simulated annealing algorithm for the clustering problem,” Pattern Recognit., vol. 10, no. 24, pp. 1003–1008, 1991.
G. P. Babu and M. N. Murty, “Simulated annealing for selecting initial seeds in the k-means algorithm,” Ind. J. Pure Appl. Math., vol. 25, pp. 85–94, 1994.
J. N. Bhuyan, V. V. Raghavan, and V. K. Elayavalli, “Genetic algorithm for clustering with an ordered representation,” in Proc. 4th Int. Conf. Genetic Algorithms. San Mateo, CA: Morgan Kaufman, 1991.
D. R. Jones and M. A. Beltramo, “Solving partitioning problems with genetic algorithms,” in Proc. 4th Int. Conf. Genetic Algorithms. San Mateo, CA: Morgan Kaufman, 1991.
G. P. Babu and M. N. Murty, “A near-optimal initial seed selection in K-means algorithm using a genetic algorithm,” Pattern Recognit. Lett., vol. 14, pp. 763–769, 1993.
Phanendra Babu G., Narasimha Murty M. “Clustering with evolution strategies,” Pattern Recognition., vol. 27, no. 2, pp. 321–329, 1994.
K. Premalatha and A.M. Natarajan, "A New Approach for Data Clustering Based on PSO with Local Search", Computer and Information Science, vol. 1, no. 4, 2008.
S Das, A Abraham and A Konar, "Automatic Clustering Using an Improved Differential Evolution Algorithm", IEEE Transactions on Systems Man and Cybernetics Part A Systems and Humans, vol. 38, no. 1, pp. 218-237, 2008.
Yi-Tung Kao, Erwie Zahara and I-Wei Kao, "A hybridized approach to data clustering", Expert Systems with Applications, vol. 34, no. 3, pp. 1754-1762, 2008.
Sultan Noman, Siti Mariyam Shamsuddin, and Aboul Ella Hassanien, "Hybrid Learning Enhancement of RBF Network with Particle Swarm Optimization", In Proceeding of 6th International Symposium on Neural Networks, pp. 19-29, 2009.
Radha Thangaraj, Millie Pant, Ajith Abraham and Pascal Bouvry, "Particle swarm optimization: Hybridization perspectives and experimental illustrations", Applied Mathematics and Computation, 2011.
Xin-She Yang, Suash Deb, "Engineering Optimisation by Cuckoo Search", Int. J. Mathematical Modelling and Numerical Optimisation, Vol. 1, No. 4, 330-343 (2010).
T. Niknam, M. Nayeripour and B.Bahmani Firouzi, "Application of a New Hybrid optimization Algorithm on Cluster Analysis ", International Journal of Electrical and Computer Engineering, vol. 4, no. 4, 2009. [28] Kennedy, J. and Eberhart, R., “Particle Swarm Optimization,” IEEE International Conference on Neural Networks (Perth, Australia), IEEE Press, 1995, pg. IV: 1942-1948.
Yang, X.-S., and Deb, S., Engineering Optimisation by Cuckoo Search, Int. J. Mathematical Modelling and Numerical Optimisation, Vol. 1, No. 4, pp.330–343, 2010.
K.C. Tan, , S.C. Chiam, A.A. Mamun, C.K. Goh “Balancing exploration and exploitation with adaptive variation for evolutionary multi-objective optimization,” European Journal of Operational Research, Vol.197, no. 2,vol.1 ,pp.701–713, September 2009.
Zhao, M., Ji, J., Liu, C., Tang, H., An improved K-Means algorithm based on bacterial foraging, (2012) International Review on Computers and Software (IRECOS), 7 (5), pp. 2546-2549.
Zakrani, A., Idri, A., Applying radial basis function neural networks based on fuzzy clustering to estimate web applications effort, (2010) International Review on Computers and Software (IRECOS), 5 (5), pp. 516-524.
Grigoras, G., Istrate, M., An efficient clustering method in evaluation of the electric energy consumption from water hydrophore stations, (2011) International Review on Modelling and Simulations (IREMOS), 4 (2), pp. 813-818.
M. V. Nageswara Rao, K. Raja Rajeswari, Design of Polyphase Sequences Using PSOCM for Target Detection with Cross Ambiguity Function, (2011) International Journal on Communications Antenna and Propagation (IRECAP), 1 (2), pp. 182-188.
Ali Khazaee, Ata Ebrahimzadeh, Electrocardiogram Beat Classification Using Support Vector Machines and Efficient Features, (2011) International Journal on Communications Antenna and Propagation (IRECAP), 1 (6), pp. 515-520.
Abbas Pirhadi, Mohammad Hassan Rahmani, HadiBahrami, Optimum Design of Aperture Coupled Microstrip Antenna Array Using Particle Swarm Optimization Method with Far Field Mutual Coupling Compensation, (2012) International Journal on Communications Antenna and Propagation (IRECAP), 2 (3), pp. 187-193.
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