### Resolution of the Feature Matching Problem Using Clustering  
Clustering methods, particularly those based on graph structures and dense regions, such as spectral clustering and density-based clustering, are widely used to address spatial variability in data [26; 27]. For instance, spatial spectral clustering techniques inspired by proximity graphs, such as epsilon neighborhood, define linkages (edges) between data points based on proximity thresholds, forming graphs that capture underlying spatial relationships. Similarly, density-based clustering methods, particularly density-based spatial clustering of applications with noise (DBSCAN) uses such proximity criteria to form dense regions. This is achieved by evaluating the number of neighboring points (minPts) within a specific distance, determined by the proximity threshold epsilon (\(\epsilon\)). This enables DBSCAN to classify data points into cores, borders, and noise while effectively identifying cluster boundaries [28]. Both spectral and density-based clustering methods often rely on graph traversal techniques, such as depth-first search (DFS), to analyze spatial relationships efficiently. By traversing graphs, these techniques group spatially continuous data points into subgraphs or clusters [29].  
In the context of feature matching, clustering techniques have demonstrated significant potential in resolving complex spatial relationships, often outperforming traditional transformation-based approaches. For instance, [PERSON] et al. [30] proposed a spatial clustering-based approach called robust feature matching using spatial clustering with heavy outliers (RFM-SCAN) that redefines the feature matching problem in images as a spatial clustering problem with outliers, modifying the DBSCAN algorithm to better handle the unique challenges in applications like remote sensing image registration. Their approach introduces adaptive clustering methods to identify motion-consistent groups while isolating mismatches, effectively managing cases where predefined transformation models, such as affine or homographic transformations, may fail due to complex and unknown geometric relationships. Moreover, [PERSON] et al. [31] applied the RFM-SCAN algorithm to aerial image registration with large view differences, addressing issues of data redundancy and sensitivity to spatial location by introducing dimensionality reduction using principal component analysis (PCA) and motion consistency constraints to recall isolated positive matches. These enhancements significantly improved RFM-SCAN performance, particularly in scenarios with high outlier rates and complex spatial variability.  
In certain problems, particularly those involving directional variability, clustering based on isotropic metrics like DBSCAN or RFM-SCAN may not fully capture this variability, leading to cluster misclassification. Anisotropic clustering methods, such as anisotropic density-based clustering with noise (ADCN) and quick unsupervised anisotropic clustering (QUAC) [32; 33], show that traditional isotropic methods often deliver less accurate results for such problems, and anisotropic methods better capture coherent and meaningful clusters by accounting for directional variability in the data.