A Multi-Agent K-Means Algorithm for Improved Parallel Data Clustering

Mohammed Ahmed Jubair - Department of Computer Technical Engineering, College of Information Technology, Imam Ja'afar Al-Sadiq University, Al-Muthanna, Iraq
Salama A. Mostafa - Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia.
Aida Mustapha - Faculty of Applied Sciences and Technology, Universiti Tun Hussein Onn Malaysia, 84600, Panchor, Johor, Malaysia.
Zirawani Baharum - Malaysian Institute of Industrial Technology, Universiti Kuala Lumpur, Persiaran Sinaran Ilmu, Bandar Seri Alam, 81750 Johor, Malaysia
Mohamad Aizi Salamat - Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia.
Aldo Erianda - Department of Information Technology, Politeknik Negeri Padang, Sumatera Barat, Indonesia


Citation Format:



DOI: http://dx.doi.org/10.30630/joiv.6.1-2.934

Abstract


Due to the rapid increase in data volumes, clustering algorithms are now finding applications in a variety of fields. However, existing clustering techniques have been deemed unsuccessful in managing large data volumes due to the issues of accuracy and high computational cost. As a result, this work offers a parallel clustering technique based on a combination of the K-means and Multi-Agent System algorithms (MAS). The proposed technique is known as Multi-K-means (MK-means). The main goal is to keep the dataset intact while boosting the accuracy of the clustering procedure. The cluster centers of each partition are calculated, combined, and then clustered. The performance of the suggested method's statistical significance was confirmed using the five datasets that served as testing and assessment methods for the proposed algorithm's efficacy. In terms of performance, the proposed MK-means algorithm is compared to the Clustering-based Genetic Algorithm (CGA), the Adaptive Biogeography Clustering-based Genetic Algorithm (ABCGA), and standard K-means algorithms. The results show that the MK-means algorithm outperforms other algorithms because it works by activating agents separately for clustering processes while each agent considers a separate group of features.

Keywords


K-means; decision-making; clustering; multi-agent system.

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