Anchor graph based connectivity peaks clustering

Clustering by fast search and find of density peaks (DPC), a classic density-based algorithm, excels in identifying clusters of arbitrary shape. However, it struggles in recognizing complex structures due to challenges in selecting density peaks and allocating non-central points. To address these issues, we propose an anchor graph based connectivity peaks clustering method which is the connection between anchor graph and density-based clustering, called AG-CPC. Firstly, it introduces a new concept of connectivity by analyzing the divergence and discreteness of neighborhood adjacency graph to detect low-density clusters and border points. Secondly, a robust two-stage assignment strategy using an adaptive parent–child relationships based on data distribution characteristics, is proposed to reduce the wrong allocation of non-central points. Lastly, a local method for constructing anchor graphs is introduced, combined with fuzzy connectivity and boundary domains of clusters, to scale down the anchor graphs and establish the connection among anchor points. The experiments demonstrate the efficiency and stability of the proposed algorithm compared to state-of-the-art algorithms on synthetic, real-world, and image datasets.