## 2 Related Work
### Density Peak Clustering Algorithm
The first step assigns the label of each data point to points near the cluster centers, while the second step completes the label assignment for the remaining points based on the nearest labeled data to each unassigned sample. [PERSON] et al. [21] proposed a connectivity-based density peak clustering algorithm called density peak clustering with connectivity estimation (DPC-CE), which selects local centers with higher relative distances to further compute cluster centers and then introduces a neighbor graph strategy to calculate the connectivity information between cluster centers. This method not only overcomes the domino effect but also adapts to clusters with uneven densities. Although the aforementioned algorithms have achieved good clustering results, they do not provide an effective solution to the problem of parameter fine-tuning within the algorithms. However, selecting appropriate parameters for data without true labels is quite challenging. In response to this issue, a parameter-free density peak clustering algorithm has also been proposed. [PERSON] et al. [22] described an optimized adaptive methodology that utilizes clustering validity indices as the objective function for the adaptive parameters of DPC. The results indicate that this approach is not only suitable for the DPC algorithm but also effective for its derivative algorithms. [PERSON] et al. [23] proposed a density-based hierarchical clustering algorithm by combining the ideas of DBSCAN and density-connectivity DBSCAN. The proposed algorithm recognizes clusters of arbitrary shape by generating a tree diagram. Inspired by the vagueness and uncertainty in clustering, [PERSON] et al. [24] proposed a concept of fuzzy density peaks, which expresses the density of a sample point as a coupling of fuzzy distances between the sample point and its neighbors. Meanwhile, an improved density peak clustering algorithm based on fuzzy operators was designed. Experiments show that the proposed algorithm performs better than most density-based clustering algorithms. [PERSON] et al. [25] modified the clustering centers of DPC by identifying the density peak points as the clustering centers. [PERSON] et al. [26] proposed a fast identification density peak method named shared-nearest-neighbor-based clustering by fast search and find of density peaks (SNN-DPC), which completes the clustering by considering the information of the nearest neighbor structure of the sample points and overcomes the problem that the traditional density peak algorithm cannot solve the problem of variable density clustering effectively, but it is sensitive to noise. Although the above algorithms effectively overcome the problems of DPC, there are still shortcomings: when the data samples have obvious density differences and are close to each other, it is difficult to find the correct clustering center in the low-density region, and the normal sample points in the low-density region can easily be identified as noise points. Compared to existing methods, the dimension density-peak-based partitioning method proposed in this paper can accurately detect cluster structures with density differences, and the dense and sparse clusters can be divided correctly when they are close to each other.