The DPC algorithm identifies cluster centers based on local density and relative distance, ignoring the influence of the sample environment on the sample point density, so it is not easy to find cluster centers in low-density areas. The single-step allocation strategy of the DPC algorithm has poor fault tolerance, and once a sample point allocation error occurs, it will lead to a series of sample point allocation errors in the follow-up. To solve the above problems, this paper proposed a density peak clustering algorithm(TNMM-DPC) based on second-order natural nearest neighbor and multi-cluster merging. Firstly, it introduced the concept of second-order natural neighbor and considered the density of the sample point and the environment of the sample point at the same time, it redefined the local density of the sample point to reduce the influence of cluster density on the selection of cluster center. Secondly, it defined the core point set to select the initial micro clusters, and allocated the sample points according to the correlation degree between the sample points and the micro clusters. Finally, it introduced the concept of neighbor boundary point set to merge the adjacent subclusters to obtain the final clustering results, avoiding the cascade effect of allocation errors. This paper compared TNMM-DPC algorithm with DPC and its improved algorithm on the artificial dataset and the UCI dataset, and the experimental results show that the TNMM-DPC algorithm can solve the problems existing in the DPC algorithm and can effectively cluster the artificial dataset and UCI dataset.

[2023-07-05] Accepted Paper /doc/2023.04.0162/v1

[2023-12-05] Printed Article /doc/2023.04.0162/v2