基于空间向量分解的边界剥离密度聚类

Border points, as an essential part of density clustering, play a key role in guiding clustering convergence and improving pattern recognition ability. Indeed, the border-peeling clustering with BP (border-peeling clustering) as the latest representative ensures the spatial isolation of core region of the cluster by using intrinsic boundary information, then enhancing the cluster backbone. Nevertheless, the performance of available methods tends to be constrained by incomplete discriminant feature, single pattern and multiple iterations. To this end, this paper proposes a novel algorithm named CBPVD (density clustering based on the border-peeling using space vector decomposition). The property of CBPVD is based on the projection subspace and original space to enhance the fine-grained feature representation of the border point from the two perspectives of sparsity (compactness) and skewness (symmetry) of distribution, then reversely establishes the cluster backbone through active boundary peeling and guides the boundary membership. Finally, we compare performance of CBPVD with six state-of-the-art methods over synthetic, UCI, and image datasets. Experiments on 40 datasets and discussion cases from 4 perspectives demonstrate that our algorithm is feasible and effective in clustering and boundary pattern recognition.