# Hybrid Clustering Algorithm Based on Improved Density Peak Clustering
## 4 Experiments
### Results  
Results on Public Datasets  
The objective of this section is to conduct a comparative analysis of clustering algorithms on four composite datasets. Figure 3 presents a comparison of the results achieved by our method against those obtained by the SNN-DPC and DPC-KNN clustering algorithms. The findings demonstrate the effectiveness of the approach outlined in this section, as it successfully assigns true labels to data instances. However, it is noteworthy that DPC-KNN, when the correct number of clusters is selected, may still encounter challenges related to the truncation distance (\(d_{c}\)) parameter in the DPC algorithm. Specifically, Figure 3b illustrates an instance where DPC-KNN erroneously clustered 116 data points. In contrast, owing to the optimization of local density and relative distance calculations utilizing shared nearest neighbors, SNN-DPC exhibits strong performance, particularly on datasets characterized by full convexity. Repeated experiments were conducted on the Compound dataset, the D31 dataset, and the Jain dataset, with the results presented in Figures 4-6, respectively. Figure 3: (**a**) The clustering results of SNN-DPC, (**b**) DPC-KNN, and (**c**) the proposed method (DPC-MS) on the Aggregation dataset. In Figure 4a, the points located on the right-hand side of the graph form a U-shaped cluster, often referred to as noise points. In such cases, neither the DPC-KNN method nor the SNN-DPC method can accurately classify these noise points. Furthermore, as observed in the results depicted in Figure 4, both the DPC-KNN and SNN-DPC methods struggle to classify circular clusters. In contrast, the DPC-MS method employs adaptive distance calculations during the classification process, leveraging local structures to preserve the primary cluster structure. In Figure 5a, SNN-DPC is found to be highly sensitive to noise points and poses challenges in parameter selection. In Figure 5b, even when setting the parameter \(k\) of DPC-KNN to match the number of clusters in the real dataset, some allocation errors persist. The reliance on local density leads to a limited ability to differentiate between closely related clusters. However, the DPC-MS method, as shown in Figure 5a while also sensitive to noise, demonstrates fewer erroneous allocation points compared to the previous two methods. Figure 6b reveals that both the SNN-DPC and DPC-KNN methods struggle to accurately classify connectivity or boundary points. The results in Figure 6b show that the DPC-KNN method fails to accurately classify multiple points (88 samples) located in the boundary region and other regions. Similarly, the SNN-DPC method also struggles to correctly classify multiple points (64 samples) located in the boundary region and other regions. In contrast, the method proposed in this article successfully classifies all points in this dataset. Based on the observations from Figures 3-6, it can be inferred that our method outperforms the SNN-DPC and DPC-KNN methods. Specifically, our method demonstrates superior performance in correctly classifying complex clusters, including connected points  
Figure 4: (**a**) The clustering results of SNN-DPC, (**b**) DPC-KNN, and (**c**) the proposed method (DPC-MS) on the Compound dataset. Figure 5: (**a**) The clustering results of SNN-DPC, (**b**) DPC-KNN, and (**c**) the proposed method (DPC-MS) on the D31 dataset. Figure 6: (**a**) The clustering results of SNN-DPC, (**b**) DPC-KNN, and (**c**) the proposed method (DPC-MS) on the Jain dataset. (located in the boundary region) and circular clusters. However, it is important to note that it may not achieve 100% accuracy in classifying the noisy sample. We conducted multiple experiments to assess the performance of the proposed methodology on various datasets and the outcomes are presented in Tables 1-3. Table 1 compares the ACC evaluation metrics of SNN-DPC, DPC-KNN, and DPC-MS. The table highlights the best results in bold, indicating that our approach outperformed others in all instances except for the E. coli dataset.