Density Peaks Clustering Algorithm Based on Natural Neighbor and Multi-Cluster Merging Strategy

DPC(Clustering by fast search and find of Density Peaks) is a simple and effective density-based clustering algorithm that requires few parameters and does not involve iterative processing. However, it also has limitations, such as sensitivity to the selection of the cutoff distance parameter\({d_c}\), and the identification of cluster centers from the decision graph involves subjectivity. Moreover, DPC demonstrates suboptimal performance on datasets with multi-density manifold structures. To address these limitations, a density peaks clustering algorithm based on natural neighbor and multi-cluster merging strategy (DPC-NaN-MS) has been proposed. Firstly, DPC-NaN-MS adaptively identifies the natural neighbor set of each data point and refines local density by incorporating geodesic distance, thereby mitigating the impact of \({d_c}\)and enhancing clustering performance on datasets with uneven density distributions. Secondly, initial subclusters are formed by searching for natural local density peaks. A novel subcluster merging strategy is introduced, which progressively intrgrates subclusters until the predefined number of clusters is reached. Experimental results on manifold datasets with uneven density distributions and complex morphologies, as well as on real-world datasets, fully demonstrate the effectiveness and superiority of DPC-NaN-MS.