Gauging- β : Border-aware hierarchical clustering based on density and proximity
Jinli Yao, Yong Zeng · Pattern Recognition · 2025
Data clustering plays a crucial role in scientific discovery and various real-world applications. However, many clustering algorithms encounter challenges that compromise their effectiveness and the accuracy of data grouping. This paper addresses three key challenges faced by most algorithms: (1) parameter setting, (2) data convexity, and (3) data separation. The proposed algorithm leverages density-based methods to identify and remove border points, effectively separating data sets. A hierarchical, single-linkage-based algorithm is then applied to the remaining points to generate the main clusters. Finally, the border points are reintegrated into the formed clusters. Experimental results demonstrate that the algorithm is capable of handling both convex and non-convex, as well as well-separated and poorly-separated, data sets. The impact of parameter settings on clustering outcomes is thoroughly investigated. Additionally, further experiments on real-world data sets reveal that the consistency of clustering results with classification labels strongly depends on an appropriate measure of sample similarity.