Enhancement of K-Means algorithm for analyzing earthquake occurrence pattern in the Philippines
Sean Marie Bayono, Ronanne Jcher Bulaon, Richard C. Regala, Vivien A. Agustin & Khatalyn E. Mata
Abstract
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.
Keywords
K-Means algorithm, mini-batch processing, disaster preparedness, seismic data analysis
Author information & Contribution
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.
"Author 1 primarily handled the implementation and development of the system and contributed to writing and editing the manuscript. Author 2 was responsible for data acquisition and contributed to drafting and revising the manuscript. Authors 3 and 4, as thesis advisers, provided critical feedback on the study’s validity, structure, and overall quality, including thorough review of formatting and content. Author 5, as the thesis coordinator, supervised the alignment of the manuscript with institutional requirements and provided guidance throughout the writing process. All authors reviewed and approved the final version of the manuscript and agreed to be accountable for all aspects of the work."
Disclosure statement
No potential conflict of interest was reported by the author(s).
Funding
This work was not supported by any funding.
AI Declaration
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.
Notes
This paper is presented in the 2nd International Student Research Congress (ISRC) 2025
Acknowledgement
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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
License:
This work is licensed under a Creative Commons Attribution (CC BY 4.0) International License.
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