This study aims to enhance the K-Means clustering algorithm to improve the analysis of earthquake occurrence patterns in the Philippines. Traditional K-Means, while effective, suffers from limitations such as random initialization and slow convergence. To address these issues, we propose an improved K-Means algorithm that strategically selects initial centroids based on a distance-weighted probability distribution to enhance accuracy and processes data in smaller batches to reduce computation time, thereby improving scalability and convergence speed. Using earthquake data from the Philippine Institute of Volcanology and Seismology (PHIVOLCS), we evaluate the performance of the enhanced algorithm using metrics such as Silhouette Score and Time Complexity. Results demonstrate that the proposed modifications significantly enhance clustering accuracy, computational efficiency, and scalability, leading to more precise identification of high-risk seismic areas. By providing a more accurate and efficient framework for seismic data analysis, this research contributes to disaster preparedness, risk mitigation, and informed decision-making in urban planning and disaster management.
K-Means algorithm, mini-batch processing, disaster preparedness, seismic data analysis
Sean Marie Bayono. Corresponding author. Undergraduate student. Department of Computer Science. College of Information Systems and Technology Management - Pamantasan ng Lungsod ng Maynila. Email: smbbayono2021@plm.edu.ph
Ronanne Jcher Bulaon. Undergraduate student. Department of Computer Science. College of Information Systems and Technology Management - Pamantasan ng Lungsod ng Maynila
Richard C. Regala. Bachelor’s Degree in Information Communication Technology. Pamantasan ng Lungsod ng Maynila. Computer Laboratory Administrator
Vivien A. Agustin. Master in Information Technology. College of Information Systems and Technology Management - Pamantasan ng Lungsod ng Maynila. Associate Dean/Assistant Professor III
Khatalyn E. Mata. Doctor in Information Technology. Dean - College of Information Systems and Technology Management, Pamantasan ng Lungsod ng Maynila.
No potential conflict of interest was reported by the author(s).
This work was not supported by any funding.
The author declares the use of Artificial Intelligence (AI) in writing this paper. In particular, the author used ChatGPT in identifying relevant literature and refining content structure. The author takes full responsibility in ensuring that research idea, analysis and interpretations are original work.
This paper is presented in the 2nd International Student Research Congress (ISRC) 2025
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Cite this article:
Bayono, S.M., Bulaon, R.J., Regala, R.C., Agustin, V.A. & Mata, K.E. (2025). Enhancement of K-Means algorithm for analyzing earthquake occurrence pattern in the Philippines. International Student Research Review, 2(1), 1-18. https://doi.org/10.53378/isrr.163
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