Enhanced Stratified Sampling-Density-Based Spatial Clustering of Applications With Noise (SS-DBSCAN) for High-Dimensional Data

This research introduces an enhanced stratified sampling-density-based spatial clustering of applications with noise (SS-DBSCAN), a scalable and robust density-based clustering algorithm designed to tackle challenges in high-dimensional and complex data analysis. The algorithm integrates advanced parameter optimization techniques to improve clustering accuracy and interpretability. Key innovations include a fast grid search method for optimizing the search of optimal minimum points (MinPts) by keeping the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>ϵ</mml:mi> </mml:math> parameter obtained constant. Notably, this study emphasizes the often-overlooked MinPts parameter, introducing a dynamic approach that initiates by calculating density metrics within a specified <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>ϵ</mml:mi> </mml:math> distance and adjusting the MinPts range based on the standard deviation of these metrics. This approach identifies optimal MinPts values based on the maximum allowed range. Comprehensive experiments on five real-world datasets demonstrate SS-DBSCAN’s superior performance compared to density-based spatial clustering of applications with noise (DBSCAN), hierarchical DBSCAN, and ordering points to identify the clustering structure (OPTICS), evidenced by higher silhouette and Davies–Bouldin index scores. The results highlight SS-DBSCAN’s ability to capture intrinsic clustering structures accurately, providing deeper insights across various research domains. SS-DBSCAN’s scalability and adaptability to diverse data densities make it a valuable tool for analyzing large, complex datasets.