## 3 Results and Discussion
All indicators demonstrate a significant advantage of the ensemble model. Thus, the ARI and NMI values were 0.9996 for the ensemble versus 0.97879 for K-Means, indicating a high match with the proper labels and greater informativeness of clustering. Similarly, the R\({}^{2}\) Score value--0.99889 for the ensemble and 0.976 for K-Means--indicates a better explanatory power of the model. Thus, the ensemble approach provides a more accurate match of the clusters to the real data structure, which all three metrics confirm. Figure 7 compares clustering errors for two approaches: the basic model using the K-Means algorithm (orange bars) and the ensemble model (blue bars) combining several methods. Root mean square error (RMSE) and mean absolute error (MAE) were employed as measures, which enabled us to compare the difference between predicted and reference values. It can be noticed that the ensemble model possesses considerably smaller error values: MAE approximates 0.00005 vs. 0.00009 for the simple model, and RMSE approximates 0.00007 vs. 0.0001. This indicates more accurate forecasting and a closer approximation of the model to the actual data distribution. Thus, the ensemble approach provides structurally more apparent clustering and reduces numerical errors, which is especially important when applying the model to regression analysis problems or quantitative assessment of features, such as the degree of soil salinity. Figure 5: Internal evaluation metrics. The higher Silhouette Score value of the ensemble model (0.76754 versus 0.61703 for the simple K-Means model) indicates better clustering quality--objects within a cluster are located more compactly, and the distances between clusters are increased. This means the proposed algorithm designs better data partitioning: objects with the same cluster are more similar, while differences between clusters are enhanced. Furthermore, the decrease from the Davies-Bouldin Score of 0.46008 by K-Means to 0.30375 of the ensemble solution indicates a tighter overlap among the clusters and an increased intra-cluster homogeneity that is especially necessary when considering multidimensional and noisy datasets. External metrics also confirm the improved clustering quality. The Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI) values of the ensemble model were 0.9996, while those of the simple model were 0.97879, demonstrating a higher correspondence between the predicted clusters and the actual labels. Similarly, the R\({}^{2}\) Score increased from 0.976 for K-Means to 0.99889 for the ensemble, indicating a better explanatory power of the model. The ensemble model also demonstrated better values in terms of errors:  
Figure 6: External evaluation metrics. Figure 7: MAE and RMSE analysis for different clustering methods. MAE decreased from 0.00009 for the simple model to 0.00005, and RMSE from 0.0001 to 0.00007. This indicates more accurate quantitative predictions and less deviation from the reference values. From a scientific point of view, all metrics are improved because the proposed approach is not limited to a single clustering algorithm. Using XGBoost trained on K-Means pseudo-labels allows us to identify hidden patterns in the data and enrich the original feature space with probabilistic characteristics, increasing its information content. The ensemble mechanism combining K-Means, agglomerative clustering, and DBSCAN allows us to consider different representations of the data structure, and voting on the results reduces the likelihood of errors associated with the shortcomings of individual algorithms. Due to the high reliability of the labels obtained at the clustering stage, some objects can be used for subsequent supervised learning. This allows us to build a model that simultaneously solves classification problems (soil type identification) and regression (quantitative assessment of the degree of salinity).