Local density based on weighted K-nearest neighbors for density peaks clustering

Density peaks clustering (DPC), a traditional density-based clustering algorithm, has received considerable attention in recent years. DPC identifies clusters by designating density peaks, defined by local density, as cluster centers. However, DPC and its variants often struggle to identify high-density peaks, particularly in datasets with arbitrarily complex shapes. To address this issue, we propose a novel local density measure based on weighted K-nearest neighbors (KNN). First, we construct a new similarity measure, termed the constrained kernel rank-order distance, to determine the KNNs of each point. Next, we develop the concept of weighted KNNs by assigning a weight to each point, representing the probability of it becoming a KNN to other points. Subsequently, we redefine the local density based on the weighted KNN. Finally, we integrate this new local density measure into the DPC framework. Experiments demonstrate that the proposed algorithm outperforms existing DPC algorithms in terms of effectiveness. The source code can be downloaded from https://github.com/Gedanke/dpcCode.