Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31821
A New Evolutionary Algorithm for Cluster Analysis

Authors: B.Bahmani Firouzi, T. Niknam, M. Nayeripour


Clustering is a very well known technique in data mining. One of the most widely used clustering techniques is the kmeans algorithm. Solutions obtained from this technique depend on the initialization of cluster centers and the final solution converges to local minima. In order to overcome K-means algorithm shortcomings, this paper proposes a hybrid evolutionary algorithm based on the combination of PSO, SA and K-means algorithms, called PSO-SA-K, which can find better cluster partition. The performance is evaluated through several benchmark data sets. The simulation results show that the proposed algorithm outperforms previous approaches, such as PSO, SA and K-means for partitional clustering problem.

Keywords: Data clustering, Hybrid evolutionary optimization algorithm, K-means algorithm, Simulated Annealing (SA), Particle Swarm Optimization (PSO).

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2116


[1] Yi-Tung Kao, Erwie Zahara and I-Wei Kao, "A hybridized approach to data clustering" Expert Systems with Applications, Volume 34, Issue 3, pp. 1754-1762, April 2008.
[2] J.F. Lu, J.B. Tang, Z.M. Tang and J.Y. Yang, "Hierarchical initialization approach for K-Means clustering", Pattern Recognition Letters, January 2008.
[3] Hong Zhou and Yonghuai Liu, "Accurate integration of multi-view range images using k-means clustering", Pattern Recognition, Volume 41, Issue 1, pp. 152-175, January 2008.
[4] Michael Laszlo and Sumitra Mukherjee , "A genetic algorithm that exchanges neighboring centers for k-means clustering", Pattern Recognition Letters, Volume 28, Issue 16, pp. 2359-2366, 1 December 2007.
[5] Amir Ahmad and Lipika Dey , "A k-mean clustering algorithm for mixed numeric and categorical data", Data & Knowledge Engineering, Volume 63, Issue 2, pp. 503-527, November 2007.
[6] Chung-Chian Hsu, Chin-Long Chen and Yu-Wei Su , "Hierarchical clustering of mixed data based on distance hierarchy", Information Sciences, Volume 177, Issue 20, 15, pp. 4474-4492, October 2007.
[7] Mohammad Fathian and Babak Amiri, "A honey-bee mating approach on clustering", The International Journal of Advanced Manufacturing Technology, DOI 10.1007/s00170-007-1132-7.
[8] Stephen J. Redmond and Conor Heneghan , "A method for initialising the K-means clustering algorithm using kd-trees", Pattern Recognition Letters, Volume 28, Issue 8, pp. 965-973, June 2007.
[9] Georgios P. Papamichail and Dimitrios P. Papamichail , "The k-means range algorithm for personalized data clustering in e-commerce", European Journal of Operational Research, Volume 177, Issue 3, pp. 1400-1408, March 2007
[10] R.J. Kuo, H.S. Wang, Tung-Lai Hu and S.H. Chou , "Application of ant K-means on clustering analysis", Computers & Mathematics with Applications, Volume 50, Issues 10-12, pp. 1709-1724, November- December 2005.
[11] P. S. Shelokar, V. K. Jayaraman and B. D. Kulkarni ",An ant colony approach for clustering ",Analytica Chimica Acta, Volume 509, Issue 2, pp. 187-195, May 2004.
[12] Michael K. Ng and Joyce C. Wong , "Clustering categorical data sets using tabu search techniques", Pattern Recognition, Volume 35, Issue 12, December 2002, pp. 2783-2790
[13] C. S. Sung and H. W. Jin, "A tabu-search-based heuristic for clustering", Pattern Recognition, Volume 33, Issue 5, pp. 849-858, May 2000.
[14] Khaled S. Al-Sultan, "A Tabu search approach to the clustering problem", Pattern Recognition, Volume 28, Issue 9, pp. 1443- 1451, September 1995.
[15] Michael Laszlo and Sumitra Mukherjee, "A genetic algorithm that exchanges neighboring centers for k-means clustering" Pattern Recognition Letters, Volume 28, Issue 16, pp. 2359-2366, December 2007
[16] J. Kennedy and R. Eberhart, "Particle Swarm Optimization," IEEE International Conf. on Neural Networks, Piscataway, NJ, vol. 4, pp. 1942-1948, 1995.
[17] J. Olamaei , T. Niknam and G. Gharehpetian, "Application of Particle Swarm Optimization for Distribution Feeder Reconfiguration Considering Distributed Generators", accepted for future publication in Applied Mathematics and Computation journal, 2008.
[18] Tai-Hsi Wu, Chin-Chih Chang and Shu-Hsing Chung, "A simulated annealing algorithm for manufacturing cell formation problems", Expert Systems with Applications, Volume 34, Issue 3, April 2008, Pages 1609-1617.
[19] Klaus Meer, "Simulated Annealing versus Metropolis for a TSP instance" Information Processing Letters, Volume 104, Issue 6, 16 December 2007, Pages 216-219