Robust Density Peaks Clustering for Manifold Data with Multiple Peaks

Density peaks clustering (DPC) is an excellent clustering algorithm that does not need any prior knowledge. However, DPC still has the following shortcomings: (1) The Euclidean distance used by it is not applicable to manifold data with multiple peaks. (2) The local density calculation for DPC is too simple, and the final results may fluctuate due to the cutoff-distance dc. (3) Manually selected centers by decision-graph may lead to a wrong number of clusters and poor performance. To address these shortcomings and improve the performance, a robust density peaks clustering algorithm for manifold data with multiple peaks (RDPCM) is proposed to reduce the sensitivity of clustering results to parameters. Motivated by DPC-GD, RDPCM replaces the Euclidean distance with geodesic distance, which is optimized by the improved mutual K-nearest neighbors. It better considers the local manifold structure of the datasets and obtains excellent results. In addition, the Davies-Bouldin Index based on Minimum Spanning Tree (MDBI) is proposed to select the ideal number of classes adaptively. Numerous experiments have established that RDPCM is more effective and superior than other advanced clustering algorithms.