Shahrood University of Technology Journal of Mining and Environment 2251-8592 17 3 2026 05 01 Comparison of Unsupervised Multivariate Clustering Methods for the Geochemical and Geospatial Characterization of Mining Tailings 801 828 3626 10.22044/jme.2025.16568.3239 EN Marco Antonio Cotrina Teatino Department of Mining Engineering, Faculty of Engineering, National University of Trujillo, Trujillo, Peru Jairo Jhonatan Marquina-Araujo Department of Mining Engineering, Faculty of Engineering, National University of Trujillo, Trujillo, Peru Jose Nestor Mamani-Quispe Faculty of Chemical Engineering, National University of the Altiplano of Puno, Puno, Peru Solio Marino Arango-Retamozo Department of Mining Engineering, Faculty of Engineering, National University of Trujillo, Trujillo, Peru 0000-0003-3594-0329 Joe Alexis Gonzalez-Vasquez Department of Industrial Engineering, Faculty of Engineering, National University of Trujillo, Trujillo, Peru Journal Article 2025 07 25 The geochemical and spatial characterization of legacy mine tailings is essential for identifying reprocessing opportunities and informing environmental management. However, the high compositional complexity of polymetallic tailings requires robust multivariate approaches. This study evaluates and compares the performance of four unsupervised clustering algorithms Euclidean K-Means, Riemannian K-Means, Gaussian Mixture Model (GMM), and Agglomerative Clustering applied to 927 samples from the Quiulacocha tailings deposit in Peru, using six major elements (Zn, Pb, Cu, Fe, Ag, Au) and spatial coordinates. All methods consistently identified three main geochemical domains. Cluster 1 was enriched in Cu and Au, Cluster 2 in Pb and Fe, and Cluster 3 in Zn, Ag, and Fe. Covariance-based methods (Riemannian K-Means and Agglomerative Clustering) outperformed others in internal validation (Silhouette scores up to 0.58) and consistency (Adjusted Rand Index = 1.00), offering more interpretable and geologically coherent partitions. CLR transformation reduced clustering performance, highlighting the importance of preserving raw geochemical variance for spatial segmentation. These findings demonstrate the effectiveness of multivariate clustering for unraveling compositional heterogeneity in tailings and delineating domains of potential economic value. The approach provides a quantitative framework for supporting reprocessing decisions, reducing risk, and guiding future research on mine waste valorization. https://jme.shahroodut.ac.ir/article_3626_d8099c3e60f2669f48669b234521d3c4.pdf