## 4 Experimental Results and Analysis
### Analysis of Algorithm Results
#### 4.3.1 Synthetic Datasets
Furthermore, the DP-CDPC algorithm exhibits a better effect than the DP-ADPC algorithm. In the case of the R15 dataset, all the values of the five external indicators of the K-means algorithm are the same, except for the DBSCAN algorithm, and larger than that of the DBSCAN algorithm. For the D31 dataset, the values of the three external evaluation indicators of the K-means algorithm are the largest compared to those of the other algorithms, followed by the SNNDPC algorithm, while the values of all the external evaluation indicators of the DP-ADPC and DP-CDPC are slightly lower than the others. Furthermore, the DP-CDPC algorithm exhibits a better effect than the DP-ADPC algorithm. In the case of the R15 dataset, all the values of the five external indicators of each algorithm are the same, except for the DBSCAN algorithm, and larger than that of the DBSCAN algorithm. For the D31 dataset, the values of the three external evaluation indicators of the K-means algorithm are the largest compared to those of the other algorithms, followed by the SNNDPC algorithm, while the values of all the external evaluation indicators of DP-ADPC and DP-CDPC are slightly lower than the others. Furthermore, the DP-CDPC algorithm exhibits a better effect than the DP-ADPC algorithm. In the case of the R15 dataset, all the values of the five external indicators of the K-means algorithm are the same, except for the DBSCAN algorithm, and larger than that of the DBSCAN algorithm. For the D31 dataset, the values of the three external evaluation indicators of the K-means algorithm are the largest compared to those of the other algorithms, followed by the SNNDPC algorithm, while the values of all the external evaluation indicators of the DP-ADPC and DP-CDPC are slightly lower than the others. Furthermore, the DP-CDPC algorithm exhibits a better effect than the DP-ADPC algorithm. In the case of the R15 dataset, all the values of the five external indicators of each algorithm are the same, except for the DBSCAN algorithm, and larger than that of the DBSCAN algorithm. For the D31 dataset, the values of the three external evaluation indicators of the K-means algorithm are the largest compared to those of the other algorithms, followed by the SNNDPC algorithm, while the values of all the external evaluation indicators of DP-ADPC and DP-CDPC are slightly lower than the others. Furthermore, the DP-CDPC algorithm exhibits a better effect than the DP-ADPC algorithm. In the case of the R15 dataset, all the values of the five external indicators of each algorithm are the same, except for the DBSCAN algorithm, and larger than that of the DBSCAN algorithm. For the D31 dataset, the values of the three external evaluation indicators of the K-means algorithm are the largest compared to those of the other algorithms, followed by the SNNDPC algorithm, while the values of all the external evaluation indicators of DP-ADPC and DP-CDPC are slightly lower than the others. the DBSCAN algorithm are the smallest. The clustering results of the ADPC and CDPC algorithms are consistent and the clustering effect is better than that of the algorithm after adding noise, among which the DP-CDPC algorithm is better than the DP-ADPC algorithm. In summary, the ADPC and CDPC algorithms demonstrate consistent clustering effects across all synthetic datasets and exhibit the best clustering performance compared to other algorithms on the flame, spiral, aggregation, and R15 datasets. In general, the DP-CDPC algorithm exhibits higher values for cluster evaluation indexes than the DP-ADPC algorithm, indicating its superior clustering effect. By comparing the clustering effect of the ADPC and CDPC algorithms, it is evident that using dichotomy to determine the truncation distance effectively addresses the problem of subjective selection. Furthermore, the CDPC algorithm shows improvement compared to the K-means, DBSCAN, and SNNDPC algorithms.