An Adaptive Density Distribution Clustering Method for Arbitrary-Shaped Datasets

Density peak clustering is an effective and interpretable method for uncovering potential knowledge in unlabeled datasets with arbitrary shapes. It has been extensively studied by researchers, and a series of extended models have been proposed. The performances of these algorithms largely depend on the positions and number of cluster centers. However, accurately selecting these centers remains a challenging problem. Therefore, to address this issue, an adaptive density distribution clustering (ADDC) method based on graph theory and $k$ -nearest neighbors is developed in this study. ADDC is a decentralized and robust clustering approach, which consists of three main components. First, an undirected neighborhood graph is constructed based on the neighbor degree defined in this article to implement a decentralized allocation strategy. Second, componentwise local density is introduced, and a new criterion for selecting density peaks is established to serve as one of the guidelines for determining the number of clusters. Third, with the neighborhood graph and density peaks, criterion-based decomposition and fusion strategies are formulated to identify clusters with multiple peaks or to detect low-density clusters without peaks. Finally, experiments and comparisons on widely used real datasets and synthetic datasets demonstrated that ADDC significantly outperforms five classical clustering methods and seven state-of-the-art density-based cluster approaches.