Density Peaks Clustering Based on Natural Search Neighbors and Manifold Distance Metric

The density-based clustering approach called Density Peaks Clustering (DPC) has been more and more popular recently. However, the original DPC algorithm is vulnerable to the parameter $d_{c}$ and is incapable of obtaining desired clustering results when dealing with manifold data sets. Furthermore, the allocation strategy of the remaining data points can easily lead to domino chain reaction which means that when a data point is allocated improperly, the points around it will be allocated incorrectly too. To address these deficiencies, we propose an algorithm named density peaks clustering based on natural search neighbors and manifold distance metric (DPC-NSN-MDM). In the beginning, we apply natural search method (NSM) to identify the natural neighbors and further calculate the local density $\rho $ of each data point. Secondly, when calculating distance $\delta $ of each data point, we put the manifold distance metric with a scaling factor to replace the traditional Euclidean distance metric. At last, manifold distance is also applied in the allocation strategy of remaining data points to reduce the effect of the domino chain reaction indirectly. The proposed DPC-NSN-MDM algorithm succeeds in getting superb experimental performance on both synthetic data sets and real-world data sets.