The week's pick
Random Articles
Reseach Article
KMGEM: Data Clustering by Combination of K-Means and Grenade Explosion Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 147 - Number 14 |
| Year of Publication: 2016 |
| Authors: Parvin Ghaffarzadeh, Mohammad H. Nadimi, Akbar Nabiollahi |
10.5120/ijca2016911333
|
Parvin Ghaffarzadeh, Mohammad H. Nadimi, Akbar Nabiollahi . KMGEM: Data Clustering by Combination of K-Means and Grenade Explosion Algorithm. International Journal of Computer Applications. 147, 14 ( Aug 2016), 21-29. DOI=10.5120/ijca2016911333
Abstract
The main purpose of using clustering techniques is to divide a dataset into a few unsupervised data analysis partitions. One of the recent and apparently one of the easiest one of them is k-means. This technique is based on square error criterion. To solve the combinatorial optimization issues in the context of clustering techniques, k-means algorithm was used recently. In spite of the fact that it has been applied to a few territories, it experiences sensitivity to initial points. There have been a few techniques that were reported beneficial for improving k-means systems. By this paper we are trying to suggest a new algorithm which depends on an optimized clustering method. This algorithm that is called K-Means Modified Grenade Explosion Method (KMGEM) is a K-Means that initialized with Modified Grenade Explosion algorithm. The results showed that our proposed method is superior in comparison with methods like Genetic Algorithm, Genetic K-Means Algorithm, and k-means algorithms.
References
- Y.T. Kao, E. Zahara , I.W. Kao, A hybridized approach to data clustering, Expert Systems with Applications, 2008, vol. 34, pp. 1754-1762.
- U. Mualik, S. Bandyopadhyay, Genetic algorithm-based clustering technique, Pattern Recognition 33, 2000, pp. 1455–1465.
- B. Bahmani Firouzi, M. Sha Sadeghi, T. Niknam, A new hybrid algorithm based on PSO, SA, and K-means for cluster analysis, International Journal of Innovative Computing Information and Control 6 (4), 2010, pp. 1–10.
- C.D. Nguyen, K.J. Cios, GAKREM: a novel hybrid clustering algorithm. Information Sciences 178, 2008, pp. 4205–4227.
- M. Fathian, B. Amiri, A honey-bee mating approach on clustering. The International Journal of Advanced Manufacturing Technology 38 (7-8), 2007, pp. 809–821.
- K.R Zalik, An efficient k-means clustering algorithm. Pattern Recognition Letters 29, 2008, pp. 1385–1391.
- T. Niknam, B. Amiri, J. Olamaie, A. Arefi, An efficient hybrid evolutionary optimization algorithm based on PSO and SA for clustering. Journal of Zhejiang University Science A, 2008, doi:10.1631/jzus.A0820196.
- T. Niknam, J. Olamaie, B. Amiri, A hybrid evolutionary algorithm based on ACO and SA for cluster analysis, Journal of Applied Science 8 (15), 2008, pp. 2695–2702.
- T. Niknam, B. Bahmani Firouzi, M. Nayeripour, An efficient hybrid evolutionary algorithm for cluster analysis, World Applied Sciences Journal 4 (2), 2008, pp. 300– 307.
- T. Niknam, E. Taherian Fard, N. Pourjafarian, A. Rousta, An efficient hybrid algorithm based on modified imperialist competitive algorithm and K-means for data clustering, Engineering Applications of Artificial Intelligence, vol. 24, 2010, pp. 306-317
- M.K. Ng, J.C. Wong, Clustering categorical data sets using tabu search techniques, Pattern Recognition 35 (12) 2002, pp. 2783–2790.
- K. Krishna, M. Murty, Genetic k-means Algorithm, IEEE Transactions on Systems, Man and Cybernetics B Cybernet 29, 1999, pp. 433–439.
- M. Fathian, et al., "Application of honey-bee mating optimization algorithm on clustering," Applied Mathematics and Computation, 2007, vol. 190, pp. 1502-1513
- P.S. Shelokar, V.K. Jayaraman, B.D. Kulkarni., An ant colony approach for clustering, Analytica Chimica Acta 509 (2), 2004, pp. 187–195.
- C.S. Sung, H.W. Jin, A tabu-search-based heuristic for clustering. Pattern Recognition 33 (5), 2000, pp. 849–858.
- T. Sakai, A. Imiya, Unsupervised cluster discovery using statistics in scale space, Engineering Applications of Artificial Intelligence 22 (1), 2009, pp. 92–100.
- T. Twellmann, A.M. Baese, O. Lange, S. Foo, T.W. Nattkemper, Model-free visualization of suspicious lesions in breast MRI based on supervised and unsupervised learning. Engineering Applications of Artificial Intelligence 21 (2), 2008, pp. 129–140.
- A. Ahrari and A. A. Atai, "Grenade Explosion Method--A novel tool for optimization of multimodal functions," Applied Soft Computing, 2010, vol. 10, pp. 1132-1140.
- E. Atashpaz-Gargari, C. Lucas, Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: Proceedings of the IEEE Congress on Evolutionary Computation, Singapore, 2007a, pp. 4661–4667.
- R. J. Kuo, H. S. Wang, Tung-Lai Hu, S.H. Chou, Application of ant K-means on clustering analysis," Computers & Mathematics with Applications, 2005, vol. 50, pp. 1709-1724.
- M. Laszlo, S. Mukherjee, A genetic algorithm that exchanges neighboring centers for k-means clustering. Pattern Recognition Letters 28 (16), 2007, pp. 2359–2366.
- Ch. Li, L. Sun, J. Jia, Y. Cai, X. Wang, Risk assessment of water pollution sources based on an integrated k-means clustering and set pair analysis method in the region of Shiyan, China, Science of the Total Environment 557–558 (2016) 307–316
Index Terms
Keywords