A domain density peak clustering algorithm based on natural neighbor

Abstract

Density peaks clustering (DPC) is as an efficient algorithm due for the cluster centers can be found quickly. However, this approach has some disadvantages. Firstly, it is sensitive to the cutoff distance; secondly, the neighborhood information of the data is not considered when calculating the local density; thirdly, during allocation, one assignment error may cause more errors. Considering these problems, this study proposes a domain density peak clustering algorithm based on natural neighbor (NDDC). At first, natural neighbor is introduced innovatively to obtain the neighborhood of each point. Then, based on the natural neighbors, several new methods are proposed to calculate corresponding metrics of the points to identify the centers. At last, this study proposes a new two-step assignment strategy to reduce the probability of data misclassification. A series of experiments are conducted that the NDDC offers higher accuracy and robustness than other methods.