Grey Wolf Optimizer (GWO) Algorithm to Solve the Partitional Clustering Problem

Keywords: Data Clustering, Fuzzy C-means, Grey Wolf Optimization (GWO), K-means, K-medoids


The clustering which is an unsupervised classification method is very important for data processing applications. The main purpose of the clustering is to separate the data samples into different groups by using the similarity (or dissimilarity) between data samples. There are many conventional and heuristic algorithms which are used for the clustering problem. Nevertheless, in last years, it is seen that many new techniques are proposed and improved to solve the clustering problem. In this paper, grey wolf optimization (GWO) algorithm which is modelled according to the social behaviour of grey wolves is applied to partition the data samples by searching the optimal center of the clusters. The clustering performance of the GWO is compared with the performances of the three clustering algorithms: k-means, k-medoids and fuzzy c-means algorithms. The experiments show that the GWO algorithm has generally better results than the other clustering algorithms and can be alternatively applied on the clustering problem.


Download data is not yet available.


A. K. Jain, M. N. Murty, and P. J. Flynn, "Data clustering: a review," ACM computing surveys (CSUR), vol. 31, pp. 264-323, 1999.

M. Karakoyun and A. Babalik, "Data Clustering with Shuffled Leaping Frog Algorithm (SFLA) for Classification," in 2015 Int'l Conference on Intelligent Computing, Electronics Systems and Information Technology (ICESIT-15), Kuala Lumpur (Malaysia), 25-26 Aug 2015.

D. Karaboga and C. Ozturk, "A novel clustering approach: Artificial Bee Colony (ABC) algorithm," Applied Soft Computing, vol. 11, pp. 652-657, 2011.

S. I. Boushaki, N. Kamel, and O. Bendjeghaba, "A new quantum chaotic cuckoo search algorithm for data clustering," Expert Systems with Applications, vol. 96, pp. 358-372, 2018.

W. A. Barbakh, Y. Wu, and C. Fyfe, "Review of clustering algorithms," in Non-Standard Parameter Adaptation for Exploratory Data Analysis, ed: Springer, 2009, pp. 7-28.

H. Frigui and R. Krishnapuram, "A robust competitive clustering algorithm with applications in computer vision," Ieee transactions on pattern analysis and machine intelligence, vol. 21, pp. 450-465, 1999.

Y. Yang and J. Jiang, "Bi-weighted ensemble via HMM-based approaches for temporal data clustering," Pattern Recognition, vol. 76, pp. 391-403, 2018.

S. Zhu and L. Xu, "Many-objective fuzzy centroids clustering algorithm for categorical data," Expert Systems with Applications, vol. 96, pp. 230-248, 2018.

A. Karami and M. Guerrero-Zapata, "A fuzzy anomaly detection system based on hybrid pso-kmeans algorithm in content-centric networks," Neurocomputing, vol. 149, pp. 1253-1269, 2015.

T. Wangchamhan, S. Chiewchanwattana, and K. Sunat, "Efficient algorithms based on the k-means and Chaotic League Championship Algorithm for numeric, categorical, and mixed-type data clustering," Expert Systems with Applications, vol. 90, pp. 146-167, 2017.

N. Nidheesh, K. A. Nazeer, and P. Ameer, "An enhanced deterministic K-Means clustering algorithm for cancer subtype prediction from gene expression data," Computers in biology and medicine, vol. 91, pp. 213-221, 2017.

(Access Date: 25 Dec, 2017). UCI Machine Learning Repository. Available:

J. F. Hair, W. C. Black, B. J. Babin, R. E. Anderson, and R. L. Tatham, Multivariate data analysis vol. 5: Prentice hall Upper Saddle River, NJ, 1998.

S. N. Neyman, B. Sitohang, and S. Sutisna, "Reversible fragile watermarking based on difference expansion using manhattan distances for 2d vector map," Procedia Technology, vol. 11, pp. 614-620, 2013.

M. Luo and B. Liu, "Robustness of interval-valued fuzzy inference triple I algorithms based on normalized Minkowski distance," Journal of Logical and Algebraic Methods in Programming, vol. 86, pp. 298-307, 2017.

D. P. Mesquita, J. P. Gomes, A. H. S. Junior, and J. S. Nobre, "Euclidean distance estimation in incomplete datasets," Neurocomputing, vol. 248, pp. 11-18, 2017.

P. Arora and S. Varshney, "Analysis of K-means and K-medoids algorithm for big data," Procedia Computer Science, vol. 78, pp. 507-512, 2016.

M. Capó, A. Pérez, and J. A. Lozano, "An efficient approximation to the K-means clustering for massive data," Knowledge-Based Systems, vol. 117, pp. 56-69, 2017.

M. Karakoyun, A. Saglam, N. A. Baykan, and A. A. Altun, "Non-locally color image segmentation for remote sensing images in different color spaces by using data-clustering methods," in 5th International Conference on Advanced Technology & Sciences (ICAT'17), Istanbul (Turkey), 9-12 May, 2017.

J. Xiao, Y. Yan, J. Zhang, and Y. Tang, "A quantum-inspired genetic algorithm for k-means clustering," Expert Systems with Applications, vol. 37, pp. 4966-4973, 2010.

M. A. Rahman and M. Z. Islam, "A hybrid clustering technique combining a novel genetic algorithm with K-Means," Knowledge-Based Systems, vol. 71, pp. 345-365, 2014.

M. I. Malinen, R. Mariescu-Istodor, and P. Fränti, "K-means*: Clustering by gradual data transformation," Pattern Recognition, vol. 47, pp. 3376-3386, 2014.

Y. Marinakis, M. Marinaki, M. Doumpos, N. Matsatsinis, and C. Zopounidis, "A hybrid stochastic genetic–GRASP algorithm for clustering analysis," Operational Research, vol. 8, pp. 33-46, 2008.

T. Velmurugan, "Performance based analysis between k-Means and Fuzzy C-Means clustering algorithms for connection oriented telecommunication data," Applied Soft Computing, vol. 19, pp. 134-146, 2014.

H.-S. Park and C.-H. Jun, "A simple and fast algorithm for K-medoids clustering," Expert systems with applications, vol. 36, pp. 3336-3341, 2009.

L. Kaufman and P. Rousseeuw, "Statistical Data Analysis Based on the L1‐Norm and Related Methods," ed: North Holland: Amsterdam, 1987.

L. Kaufman and P. J. Rousseeuw, "Partitioning around medoids (program pam)," Finding groups in data: an introduction to cluster analysis, pp. 68-125, 1990.

A. Khatami, S. Mirghasemi, A. Khosravi, C. P. Lim, and S. Nahavandi, "A new PSO-based approach to fire flame detection using K-Medoids clustering," Expert Systems with Applications, vol. 68, pp. 69-80, 2017.

J. C. Dunn, "A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters," 1973.

J. C. Bezdek, "Objective Function Clustering," in Pattern recognition with fuzzy objective function algorithms, ed: Springer, 1981, pp. 43-93.

J. Jędrzejowicz and P. Jędrzejowicz, "Distance-based online classifiers," Expert Systems with Applications, vol. 60, pp. 249-257, 2016.

X. Qiu, Y. Qiu, G. Feng, and P. Li, "A sparse fuzzy c-means algorithm based on sparse clustering framework," Neurocomputing, vol. 157, pp. 290-295, 2015.

[S. Mirjalili, S. M. Mirjalili, and A. Lewis, "Grey wolf optimizer," Advances in engineering software, vol. 69, pp. 46-61, 2014.

[A. K. M. Khairuzzaman and S. Chaudhury, "Multilevel thresholding using grey wolf optimizer for image segmentation," Expert Systems with Applications, vol. 86, pp. 64-76, 2017.

[L. Rodríguez, O. Castillo, J. Soria, P. Melin, F. Valdez, C. I. Gonzalez, et al., "A fuzzy hierarchical operator in the grey wolf optimizer algorithm," Applied Soft Computing, vol. 57, pp. 315-328, 2017.

N. Singh and S. Singh, "A novel hybrid GWO-SCA approach for optimization problems," Engineering Science and Technology, an International Journal, 2017.

How to Cite
M. KARAKOYUN, O. INAN, and İhtisam AKTO, “Grey Wolf Optimizer (GWO) Algorithm to Solve the Partitional Clustering Problem”, IJISAE, vol. 7, no. 4, pp. 201-206, Dec. 2019.
Research Article