Advanced Clustering Techniques for Tin Deposit Classification in Malaysia: A Machine Learning Approach
DOI:
https://doi.org/10.17794/rgn.2025.3.10Keywords:
tin deposits, clustering techniques, geochemical analysis, Malaysia, mineral explorationAbstract
This study explores the application of advanced clustering techniques—Spectral Clustering, Gaussian Mixture Models (GMM), and a hybrid approach combining Autoencoders with K-Means—to classify tin deposits in Malaysia. Geochemical data from 28 tin samples across regions such as Pengkalan Hulu North, Menglembu, Klian Intan, and Sungai Lembing were analysed to identify distinct mineralization patterns. The results revealed that the integration of Autoencoders with K-Means yielded the highest clustering quality, with a Silhouette Score above 0.4 and a Calinski-Harabasz Index of 90 at four clusters, outperforming the other methods. The classification effectively distinguished between Pegmatite, Hydrothermal Veins, Polymetallic, and Disseminated deposits, aligning with the geological characteristics of the regions. These findings enhance the understanding of tin deposit distribution, offering significant potential for optimizing exploration strategies and mining operations, thereby contributing to the sustainable economic development of Malaysia's tin mining industry.
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Copyright (c) 2025 Meisam Saleki, Kamran Mostafaei, Zakaria Bin Endut, Mohammad Nabi Kianpour
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