A New Approach In Metaheuristic Clustering: Coot Clustering

Yıl 2024, , 59 - 67, 31.05.2024

Gökhan Kayhan İsmail İşeri

https://doi.org/10.21541/apjess.1418885

Öz

As a result of technological advancements, the increase in vast amounts of data in today's world has made artificial intelligence and data mining significantly crucial. In this context, the clustering process, which aims to explore hidden patterns and meaningful relationships within complex datasets by grouping similar features to conduct more effective analyses, holds vital importance. As an alternative to classical clustering methods that face challenges such as large volumes of data and computational complexities, a metaheuristic clustering method utilizing Coot Optimization (COOT), a swarm intelligence-based algorithm, has been proposed. COOT, inspired by the hunting stages of eagles and recently introduced into the literature, is a metaheuristic method. Through the proposed COOT metaheuristic clustering method, the aim is to contribute to the literature by leveraging COOT's robust exploration and exploitation processes, utilizing its dynamic and flexible structure. Comprehensive experimental clustering studies were conducted to evaluate the consistency and effectiveness of the COOT-based algorithm using randomly generated synthetic data and the widely used Iris dataset in the literature. The same datasets underwent analysis using the traditional clustering algorithm K-Means, renowned for its simplicity and computational speed, for comparative purposes. The performance of the algorithms was assessed using cluster validity measures such as Silhouette Global, Davies-Bouldin, Krznowski-Lai, and Calinski-Harabasz indices, along with the Total Squared Error (SSE) objective function. Experimental results indicate that the proposed algorithm performs clustering at a competitive level with K-Means and shows potential, especially in multidimensional datasets and real-world problems. Despite not being previously used for clustering purposes, the impressive performance of COOT in some tests compared to the K-Means algorithm showcases its success and potential to pioneer different studies aimed at expanding its usage in the clustering domain.

Anahtar Kelimeler

Clustering, Metaheuristic, Coot Optimization, K-Means

Kaynakça

• R. Dash and R. Dash, "Comparative Analysis of K-Means and Genetic Algorithm Based Data Clustering," International Journal of Advanced Computer and Mathematical Sciences, vol. 3, no. 2, pp. 257-265, 2012.
• V. Kumar, J. K. Chhabra, and D. Kumar, "Grey Wolf Algorithm-Based Clustering Technique," Journal of Intelligent Systems, vol. 26, no. 1, pp. 153-168, 2017.
• L. Abualigah, D. Yousri, M. Abd Elaziz, A. A. Ewees, M. A. Al-Qaness, and A. H. Gandomi, "Aquila Optimizer: A Novel Meta-heuristic Optimization Algorithm," Computers & Industrial Engineering, vol. 157, p. 107250, 2021.
• S. Çınaroğlu and H. Bulut, "K-Means and Particle Swarm Optimization-Based Novel Initialization Approaches for Clustering Algorithms," Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 33, no. 2, pp. 413-424, 2018.
• Ö. Demirkale and Ç. Özarı, "Evaluation of Fundamental Macroeconomic and Financial Indicators with K-Means Clustering Method: The Case of Fragile Five Countries," Finans Ekonomi ve Sosyal Araştırmalar Dergisi, vol. 5, no. 1, pp. 22-32, 2020. A.
• Sancho, J. C. Ribeiro, M. S. Reis, and F. G. Martins, "Cluster Analysis of Crude Oils with K-Means Based On Their Physicochemical Properties," Computers & Chemical Engineering, vol. 157, p. 107633, 2022.
• O. S. Faragallah, H. M. El-Hoseny, and H. S. El-Sayed, "Efficient Brain Tumor Segmentation using OTSU and K-Means Clustering in Homomorphic Transform," Biomedical Signal Processing and Control, vol. 84, p. 104712, 2023.
• E. A. Pambudi, A. Y. Badharudin, and A. P. Wicaksono, "Enhanced K-Means By Using Grey Wolf Optimizer for Brain MRI Segmentation," ICTACT Journal on Soft Computing, vol. 11, no. 3, 2021.
• L. Khrissi, N. El Akkad, H. Satori, and K. Satori, "Clustering Method and Sine Cosine Algorithm for Image Segmentation," Evolutionary Intelligence, pp. 1-14, 2022.
• H. Arslan and M. Toz, "Hybrid FCM-WOA Data Clustering Algorithm," in 2018 26th Signal Processing and Communications Applications Conference (SIU), pp. 1-4, IEEE, 2018.
• R. R. Mostafa, A. G. Hussien, M. A. Khan, S. Kadry and F. A. Hashim, "Enhanced COOT optimization algorithm for Dimensionality Reduction," 2022 Fifth International Conference of Women in Data Science at Prince Sultan University (WiDS PSU), Riyadh, Saudi Arabia, pp. 43-48, 2022.
• A. Ozden., I. İseri, “COOT optimization algorithm on training artificial neural networks,” Knowledge and Information Systems , vol. 65, pp.3353–3383 , 2023.
• D.S. Irene, M. Lakshmi, A.M.J. Kinol, et al. “Improved deep convolutional neural network-based COOT optimization for multimodal disease risk prediction.”, Neural Comput & Applic, vol. 35, pp.1849–1862, 2023.
• M. Aslan, İ. Koç, “Modified Coot bird optimization algorithm for solving community detection problem in social networks.” , Neural Comput & Applic , vol. 36, pp.5595–5619, 2024.
• X. You, G. Yan, M. Thwin, ”Applying modified coot optimization algorithm with artificial neural network meta-model for building energy performance optimization: A case study”,Heliyon,vol.9, no 6, p.e16593, 2023.
• I. Koc, “A fast community detection algorithm based on coot bird metaheuristic optimizer in social networks”, Engineering Applications of Artificial Intelligence, vol .14, p. 105202, 2022.
• E. Pashaei, E. Pashaei,. “Hybrid binary COOT algorithm with simulated annealing for feature selection in high-dimensional microarray data”.,Neural Comput & Applic,vol. 35, pp.353–374, 2023.
• D. Jabbar Luaibi., “Precise Classification of Brain Magnetic Resonance Imaging (MRIs) using COOT optimization.”, Texas Journal of Engineering and Technology, vol.26, pp.57–71, 2023.
• D.S. Irene, M. Lakshmi, A.M.J. Kinol, et al. “Improved deep convolutional neural network-based COOT optimization for multimodal disease risk prediction.”, Neural Comput & Applic vol.35, pp.1849–1862 ,2023.
• R. A. Fisher, "Iris," UCI Machine Learning Repository, 1988. https://doi.org/10.24432/C56C76.
• Y. Liu, Z. Li, H. Xiong, X. Gao, ve J. Wu, "Understanding of Internal Clustering Validation Measures," in 2010 IEEE International Conference On Data Mining, pp. 911-916, December 2010.
• W.J. Krzanowski ve Y.T. Lai, "A Criterion for Determining the Number of Groups in a Data Set Using Sum-of-Squares Clustering," Biometrics, vol. 44, s. 23, 1988.
• A. A. R. Fernandes, F. U. Solimun, A. Aryandani, A. Chairunissa, A. Alifa, E. Krisnawati, ..., F. L. N. Rasyidah12, "Comparison of Cluster Validity Index using Integrated Cluster Analysis with Structural Equation Modeling the War-Pls Approach," Journal of Theoretical and Applied Information Technology, vol. 99, no. 18, 2021.