The Adaptation of Gray Wolf Optimizer to Data Clustering

Year 2022, Volume: 25 Issue: 4, 1761 - 1767, 16.12.2022

Adem Tekerek Murat Dörterler

https://doi.org/10.2339/politeknik.778630

Cited By: 3

Abstract

Data Clustering stands for a group of methods classifying patterns into groups and retrieving similarities or dissimilarities of a collection of objects. Clustering is used for pattern recognition, machine learning, etc. One of the approaches to clustering is optimization. The aim of the optimization is finding the best solution in the search space of a problem as much as possible. Many optimization methods were modified to solve clustering problems in literature. Gray Wolf Optimizer (GWO) is one of the nature-inspired meta-heuristic algorithms simulating the hunting of gray wolves. GWO has applied to solve several optimization issues in different fields. In this study, GWO was examined in the case of data clustering. GWO was modified to get better clustering results and applied to well-known benchmark data sets. The performance of GWO was compared to the other algorithms used as clustering. The results show that GWO can be used for data clustering successfully.

Keywords

Data clustering, meta-heuristic optimization, gray wolf optimizer, data mining

Bozkurt Optimizasyon Yönteminin Veri Kümelemeye Uyarlanması

Abstract

Veri Kümeleme, veri desenlerini gruplar halinde sınıflandıran ve bir nesne benzerliklerini veya farklılıklarını ayrıştıran bir yöntemlerdir. Kümeleme, örüntü tanıma, makine öğrenimi vb. için kullanılır. Veri Kümelemeye yönelik yaklaşımlardan biri de optimizasyondur. Optimizasyonun amacı, bir problemin arama alanında mümkün olan en iyi çözümün bulunmasıdır. Literatürdeki kümeleme problemlerini çözmek için birçok optimizasyon yöntemi uyarlanmıştır. Bozkurt Optimizasyonu (BO), boz kurtların avlanmasını simüle eden doğadan ilham alan sezgi ötesi algoritmalardan biridir. BO, farklı alanlardaki çeşitli optimizasyon sorunlarına başarılı çözüm üretmektedir. Bu çalışmada BO, veri kümeleme için incelenmiştir. BO, daha iyi kümeleme sonuçları elde etmek için değiştirilerek, iyi bilinen veri kümelerine kıyaslama amacıyla uygulanmıştır. BO'nun performansı, kümeleme olarak kullanılan diğer algoritmalarla karşılaştırılmıştır. Sonuçlar, BO'nun veri kümeleme için başarıyla kullanılabileceğini göstermektedir.

Keywords

Veri kümeleme, meta-sezgisel optimizasyon, bozkurt kurt optimizasyonu, veri madenciliği

References

• [1] Barbakh, W., Wu, Y., Fyfe, C., “Review of clustering algorithms”, Non-Standard Parameter Adaptation for Exploratory Data Analysis, Springer, Berlin Heidelberg, 7–28, (2009).
• [2] Han, J., Kamber, M., “Data Mining: Concepts and Techniques”, Academic Press, (2006).
• [3] Jain, A.K., “Data clustering: 50 years beyond K-means”, Pattern Recognition Letters 31: 651–666, (2010).
• [4] Evangelou, I. E., Hadjimitsis, D. G., Lazakidou, A. A., Clayton, C., “Data Mining and Knowledge Discovery in Complex Image Data using Artificial Neural Networks”, Workshop on Complex Reasoning an Geographical Data, Cyprus, (2001).
• [5] Andrews, H. C., “Introduction to Mathematical Techniques in Pattern Recognition”, John Wiley & Sons, New York, (1972).
• [6] Topaloglu, N., “Revised: Finger print classification based on gray-level fuzzy clustering co-occurrence matrix”, Energy Education Science and Technology Part A: Energy Science and Research, 31(3): 1307-1316, (2013).
• [7] Sakar, B. E., Isenkul, M. E., Sakar, C. O., Sertbas, A., Gurgen, F., Delil, S., Kursun, O., “Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings”. IEEE Journal of Biomedical and Health Informatics, 17(4): 828-834, (2013).
• [8] Mo, H. J., & White, S. D., “An analytic model for the spatial clustering of dark matter haloes”, Monthly Notices of the Royal Astronomical Society, 282(2): 347-361, (1996).
• [9] Yeung, K. Y., Haynor, D. R., Ruzzo, W. L., “Validating clustering for gene expression data”, Bioinformatics, 17(4): 309-318, (2001).
• [10] Rao, M. R., “Cluster Analysis and Mathematical Programming”, Journal of the American Statistical Association, 22: 622-626, (1971).
• [11] Hatamlou, A., “Black hole: A new heuristic optimization approach for data clustering”, Information sciences, 222: 175-184, (2013).
• [12] Jain , A.K., Murty, M.N., Flynn, P.J., “Data clustering: a review”, Computing Surveys, ACM, 264–323, (1999).
• [13] Liu, Y., Yi, Z., Wu, H., Ye, M., Chen, K., “A tabu search approach for the minimum sum-of-squares clustering problem”, Information Sciences, 178: 2680–2704, (2008).
• [14] Liu, R., Jiao, L., Zhang, X., Li, Y., “Gene transposon based clone selection algorithm for automatic clustering”, Information Sciences, 204: 1–22, (2012).
• [15] Maulik, U., Bandyopadhyay, S., “Genetic algorithm-based clustering technique”, Pattern Recognition, 33: 1455–1465, (2000).
• [16] Maulik , U., Bandyopadhyay, S., “Fuzzy partitioning using a real-coded variable-length genetic algorithm for pixel classification”, IEEE Transactions on Geoscience and Remote Sensing, 41, 1075–1081, (2003).
• [17] Murthy , C.A., Chowdhury, N., “In search of optimal clusters using genetic algorithms”, Pattern Recognition Letters, 17: 825–832, (1996).
• [18] A. Ghosh, A. Halder, M. Kothari, S. Ghosh, Aggregation pheromone density based data clustering, Information Sciences 178 (2008) 2816–2831.
• [19] Niknam , T., Amiri, B., “An efficient hybrid approach based on PSO, ACO and K-means for cluster analysis”, Applied Soft Computing, 10: 183–197, (2010).
• [20] Zhang, L., Cao, Q., “A novel ant-based clustering algorithm using the kernel method”, Information Sciences, 181: 4658-4672, (2010).
• [21] Fathian, M., Amiri, B., Maroosi, A., “Application of honey-bee mating optimization algorithm on clustering”, Applied Mathematics and Computation, 190: 1502–1513, (2007).
• [22] Ahmadi , A., Karray, F., Kamel, M.S., “Model order selection for multiple cooperative swarms clustering using stability analysis”, Information Sciences, 182: 169–183, (2012).
• [23] Izakian, H., Abraham, A., “Fuzzy C-means and fuzzy swarm for fuzzy clustering problem”, Expert Systems with Applications, 38: 1835–1838, (2011).
• [24] Kuo, R.J., Syu, Y.J., Chen, Z.-Y., Tien, F.C., “Integration of particle swarm optimization and genetic algorithm for dynamic clustering”, Information Sciences, 195: 124–140, (2012).
• [25] Karaboga, D., Ozturk, C., “A novel clustering approach: artificial bee colony (ABC) algorithm”, Applied Soft Computing, 11: 652–657, (2011).
• [26] Hatamlou, A., Abdullah, S., Nezamabadi-Pour, H., “Application of gravitational search algorithm on data clustering”, In International conference on rough sets and knowledge technology, Springer, Berlin, Heidelberg, 337-346, (2011).
• [27] Hatamlou, A., Abdullah, S., Nezamabadi-Pour, H. “A combined approach for clustering based on K-means and gravitational search algorithms”, Swarm and Evolutionary Computation, 6: 47-52, (2012).
• [28] Hatamlou, A., “In search of optimal centroids on data clustering using a binary search algorithm”, Pattern Recognition Letters, 33: 1756–1760, (2012).
• [29] Hatamlou, A., Abdullah, S., Hatamlou, M., “Data clustering using big bang–big crunch algorithm”, Communications in Computer and Information Science, 383–388, (2011).
• [30] El-Abd, M., “Performance assessment of foraging algorithms vs. evolutionary algorithms”, Information Sciences, 182: 243–263, (2012).
• [31] Ghosh, S., Das, S., Roy, S., Minhazul Islam, S.K., Suganthan, P.N., “A differential covariance matrix adaptation evolutionary algorithm for real parameter optimization”, Information Sciences, 182: 199–219, (2012).
• [32] Rana, S., Jasola , S., Kumar, R., “A review on particle swarm optimization algorithms and their applications to data clustering”, Artificial Intelligence Review, 35: 211–222, (2011).
• [33] Yeh, W. C., “Novel swarm optimization for mining classification rules on thyroid gland data”, Information Sciences, 197: 65–76, (2012).
• [34] Fox, B., Xiang, W., Lee, H., “Industrial applications of the ant colony optimization algorithm”, The International Journal of Advanced Manufacturing Technology 31: 805–814, (2007).
• [35] Cisty, M., “Application of the harmony search optimization in irrigation”, In Recent Advances in Harmony Search Algorithm, Springer, Berlin, Heidelberg, 123-134, (2010).
• [36] Christmas, J., Keedwell, E., Frayling, T. M., & Perry, J. R., “Ant colony optimisation to identify genetic variant association with type 2 diabetes”, Information Sciences, 181(9): 1609-1622, (2011).
• [37] Zhang, Y., Gong, D. W., & Ding, Z., “A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch”, Information sciences, 192: 213-227, (2012).
• [38] Atila, U., Dörterler, M., Durgut, R., Sahin, İ., “A comprehensive investigation into the performance of optimization methods in spur gear design”, Engineering Optimization, 1-16, (2019).
• [39] Mirjalili , S., Mirjalili, S. M., Lewis A., “Grey wolf optimizer”, Advances in Engineering Software, 69: 46–61, (2014).
• [40] Wine Data Set, https://archive.ics.uci.edu/ml/datasets/wine, Access Time : 19.11.2020
• [41] Iris Data Set, https://archive.ics.uci.edu/ml/datasets/iris , Access Time : 19.11.2020
• [42] Breast Cancer Wisconsin (Original) Data Set, https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original) , Access Time : 19.11.2020
• [43] Connectionist Bench (Vowel Recognition - Deterding Data) Data Set, https://archive.ics.uci.edu/ml/datasets/Connectionist+Bench+(Vowel+Recognition+-+Deterding+Data) , Access Time : 19.11.2020
• [44] Glass Identification Data Set, https://archive.ics.uci.edu/ml/datasets/glass+identification , Access Time : 19.11.2020
• [45] “Contraceptive Method Choice Data Set”, https://archive.ics.uci.edu/ml/datasets/Contraceptive+Method+Choice , Access Time : 19.11.2020
• [46] Kennedy , J., Eberhart, R., “Particle swarm optimization”, in: Proceedings of IEEE International Conference on Neural Networks, 1944: 1942–1948, (1995).
• [47] Rashedi, E., Nezamabadi-pour, H., Saryazdi, S., “GSA: a gravitational search algorithm”, Information Sciences 179: 2232–2248, (2009).
• [48] Erol, O.K., Eksin, I., “A new optimization method: big bang–big crunch”, Advances in Engineering Software 37: 106–111, (2006)
• [49] Mech, L. D., “Alpha status, dominance, and division of labor in wolf packs”, Canadian Journal of Zoology-Revue Canadienne De Zoologie, 77: 1196–1203, (1999).
• [50] Muro, C., Escobedo, R., Spector, L., Coppinger, R., ”Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations”, Behav Process, 88: 192-197, (2011).