Application of a New Hybrid Optimization Algorithm on Cluster Analysis
Clustering techniques have received attention in many areas including engineering, medicine, biology and data mining. The purpose of clustering is to group together data points, which are close to one another. The K-means algorithm is one of the most widely used techniques for clustering. However, K-means has two shortcomings: dependency on the initial state and convergence to local optima and global solutions of large problems cannot found with reasonable amount of computation effort. In order to overcome local optima problem lots of studies done in clustering. This paper is presented an efficient hybrid evolutionary optimization algorithm based on combining Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), called PSO-ACO, for optimally clustering N object into K clusters. The new PSO-ACO algorithm is tested on several data sets, and its performance is compared with those of ACO, PSO and K-means clustering. The simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for handing data clustering.
[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, Babak Amiri and Ali Maroosi , "Application of
honey-bee mating optimization algorithm on clustering", Applied
Mathematics and Computation, Volume 190, Issue 2, pp. 1502-1513,
July 2007.
[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. 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.
[17] J. Kennedy and R. Eberhart, "Particle Swarm Optimization," IEEE
International Conf. on Neural Networks, Piscataway, NJ, vol. 4, pp. 1942-
1948, 1995.
[18] T. Niknam, "An Approach Based on Particle Swarm Optimization for
Optimal Operation of Distribution Network Considering Distributed
Generators,", 32nd Annual Conference onIEEE Industrial Electronics,
IECON 2006 , pp. :633 - 637
[19] T. Niknam , A.M. Ranjbar and A.R. Shirani, "A new approach for
distribution state estimation based on ant colony algorithm with regard to
distributed generation" Journal of Intelligent and Fuzzy Systems, vol. 16,
no. 2, pp.119-131, June 2005.
[20] T.Niknam, H. Arabian and M. Mirjafari, "Reactive power pricing in
deregulated environments using novel search methods" Proceedings of
2004 International Conference on Machine Learning and Cybernetics,
2004, vol. 7, p.p. 4234 - 4239, Aug. 2004
[21] T. Niknam, A.M. Ranjbar, and A.R. Shirani, "A new approach based on
ant algorithm for Volt/Var control in distribution network considering
distributed generation," Iranian Journal of Science & Technology,
Transaction B, vol. 29, no. B4, pp. 1-15, 2005.
[22] M. Dorigo, G.D. Caro and L.M. Gambardella, "Ants algorithms for
discrete optimization", Artificial Life, vol. 5, no. 3, pp. 137-172, 1999.
[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, Babak Amiri and Ali Maroosi , "Application of
honey-bee mating optimization algorithm on clustering", Applied
Mathematics and Computation, Volume 190, Issue 2, pp. 1502-1513,
July 2007.
[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. 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.
[17] J. Kennedy and R. Eberhart, "Particle Swarm Optimization," IEEE
International Conf. on Neural Networks, Piscataway, NJ, vol. 4, pp. 1942-
1948, 1995.
[18] T. Niknam, "An Approach Based on Particle Swarm Optimization for
Optimal Operation of Distribution Network Considering Distributed
Generators,", 32nd Annual Conference onIEEE Industrial Electronics,
IECON 2006 , pp. :633 - 637
[19] T. Niknam , A.M. Ranjbar and A.R. Shirani, "A new approach for
distribution state estimation based on ant colony algorithm with regard to
distributed generation" Journal of Intelligent and Fuzzy Systems, vol. 16,
no. 2, pp.119-131, June 2005.
[20] T.Niknam, H. Arabian and M. Mirjafari, "Reactive power pricing in
deregulated environments using novel search methods" Proceedings of
2004 International Conference on Machine Learning and Cybernetics,
2004, vol. 7, p.p. 4234 - 4239, Aug. 2004
[21] T. Niknam, A.M. Ranjbar, and A.R. Shirani, "A new approach based on
ant algorithm for Volt/Var control in distribution network considering
distributed generation," Iranian Journal of Science & Technology,
Transaction B, vol. 29, no. B4, pp. 1-15, 2005.
[22] M. Dorigo, G.D. Caro and L.M. Gambardella, "Ants algorithms for
discrete optimization", Artificial Life, vol. 5, no. 3, pp. 137-172, 1999.
@article{"International Journal of Engineering, Mathematical and Physical Sciences:59789", author = "T. Niknam and M. Nayeripour and B.Bahmani Firouzi", title = "Application of a New Hybrid Optimization Algorithm on Cluster Analysis", abstract = "Clustering techniques have received attention in many areas including engineering, medicine, biology and data mining. The purpose of clustering is to group together data points, which are close to one another. The K-means algorithm is one of the most widely used techniques for clustering. However, K-means has two shortcomings: dependency on the initial state and convergence to local optima and global solutions of large problems cannot found with reasonable amount of computation effort. In order to overcome local optima problem lots of studies done in clustering. This paper is presented an efficient hybrid evolutionary optimization algorithm based on combining Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), called PSO-ACO, for optimally clustering N object into K clusters. The new PSO-ACO algorithm is tested on several data sets, and its performance is compared with those of ACO, PSO and K-means clustering. The simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for handing data clustering.
", keywords = "Ant Colony Optimization (ACO), Data clustering, Hybrid evolutionary optimization algorithm, K-means clustering, Particle Swarm Optimization (PSO).", volume = "2", number = "10", pages = "734-6", }
