Supplementary Material for "Revealing Spatiotemporal Deformation Patterns through Time-Dependent Clustering of GNSS Data in the Japanese Islands"

Supplementary Material for the articleGabsatarov, Y.; Vladimirova, I.; Ignatyev, D.; Scheveva, N.“Revealing Spatiotemporal Deformation Patterns through Time-Dependent Clustering of GNSS Data in the Japanese Islands”submitted to MDPI Machine Learning and Knowledge Extraction (MAKE). This archive contains figures and analysis outputs related to the clustering of Japanese GNSS (GEONET) data, including: Results of clustering analysis of the steady-state velocity field (Figures S1–S5). Results of clustering analysis of regression-derived parameters (Figures S6–S10). Calculated plate motion velocities. Data and figures include feature correlation heatmaps, PCA explained variance, cluster evaluation metrics, pairwise ARI heatmaps, and ARI-based stability analyses for different feature sets.