Title: Density-Aware Hierarchical Framework for Point Set Representations
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: points set, representation, hierarchical cluster, multidimension

Abstract: This paper introduces a novel hierarchical framework for representing point sets using density-based clustering and relative geometric encoding. By integrating DBSCAN for adaptive partitioning and recursive centroid computation, our approach addresses limitations in existing methods such as sensitivity to sampling density and fixed neighborhood parameters. Evaluations on synthetic datasets demonstrate scalability and robustness across dimensions, paving the way for applications in 3D point cloud analysis.