## 4 Experimental Results and Analysis
### Analysis of Algorithm Results
#### 4.3.2 UCI Datasets  
This section further verifies the effect of the DP-CDPC algorithm on the UCI datasets, and the experimental results of each algorithm on the UCI datasets are shown in Table 6. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Algorithm** & _ARI_ & _AMI_ & _FMI_ \\ \hline \multirow{6}{*}{seeds} & K-means & 0.773 & 0.725 & 0.848 \\  & DBSCAN & 0.408 & 0.453 & 0.648 \\  & SNNDPC & 0.745 & 0.737 & 0.830 \\  & ADPC & **0.800** & **0.754** & **0.866** \\  & CDPC & **0.800** & **0.754** & **0.866** \\  & DP-ADPC & 0.768 & 0.727 & 0.846 \\  & DP-CDPC & 0.771 & 0.726 & 0.847 \\ \hline \multirow{6}{*}{ecoli} & K-means & 0.500 & 0.618 & 0.620 \\  & DBSCAN & 0.463 & 0.444 & 0.638 \\  & SNNDPC & 0.674 & 0.630 & 0.769 \\  & ADPC & 0.671 & 0.633 & 0.772 \\  & CDPC & 0.671 & 0.633 & 0.772 \\  & DP-ADPC & 0.647 & 0.621 & 0.749 \\  & DP-CDPC & **0.687** & **0.646** & **0.781** \\ \hline \multirow{6}{*}{movement} & K-means & 0.336 & 0.564 & 0.383 \\  & DBSCAN & 0.255 & 0.479 & 0.299 \\  & SNNDPC & 0.278 & 0.462 & 0.342 \\  & ADPC & **0.394** & 0.595 & **0.436** \\  & CDPC & 0.391 & **0.618** & 0.441 \\  & DP-ADPC & 0.367 & 0.580 & 0.416 \\  & DP-CDPC & 0.364 & 0.588 & 0.423 \\ \hline \multirow{6}{*}{dermatology} & K-means & 0.698 & 0.856 & 0.758 \\  & DBSCAN & 0.440 & 0.631 & 0.583 \\ \cline{1-1}  & SNNDPC & 0.825 & 0.883 & 0.868 \\ \cline{1-1}  & ADPC & 0.837 & 0.900 & 0.877 \\ \cline{1-1}  & CDPC & 0.837 & 0.900 & 0.877 \\ \cline{1-1}  & DP-ADPC & 0.837 & 0.901 & 0.879 \\ \cline{1-1}  & DP-CDPC & **0.852** & **0.911** & **0.889** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The clustering evaluation index of each algorithm for UCI datasets. An analysis of Table 6 reveals the following observations. For the seeds dataset, the clustering results of the ADPC and CDPC algorithms are consistent, and they achieve the largest values for each external evaluation index among all the algorithms. The DP-ADPC and DP-CDPC algorithms yield lower values for each evaluation index compared to the CDPC algorithm. However, the clustering effectiveness of the DP-CDPC algorithm is better than that of the DP-ADPC algorithm. In the case of the ecoli dataset, the clustering effect of the CDPC and ADPC algorithms is the same and superior to the K-means, DBSCAN, SNNDPC, and DP-ADPC algorithms. However, the value of \(ARI\) is slightly lower than that of the SNNDPC algorithm. The DP-CDPC algorithm outperforms other algorithms in terms of each clustering evaluation index, demonstrating its effectiveness. Regarding the movement dataset, the values of \(ARI\) and \(FMI\) for the ADPC algorithm are higher than those of other algorithms, while the value of \(AMI\) for the CDPC algorithm is the largest among all the algorithms. Adding appropriate noise to these algorithms will have a certain impact on their clustering effect. The values of \(ARI\) and \(FMI\) for the DP-ADPC algorithm are larger than those of the DP-CDPC algorithm, but the value of \(AMI\) is lower than that of the DP-CDPC algorithm. In terms of the dermatology dataset, the clustering evaluation indexes of the CDPC and ADPC algorithms are the same and slightly lower than those of the DP-CDPC and DP-ADPC algorithms. However, they are higher than other algorithms, indicating the superior clustering effect of the DP-CDPC algorithm. For the banknote dataset, the clustering evaluations of the CDPC and ADPC algorithms are consistent and superior to other algorithms. The clustering evaluations of the DP-CDPC algorithm are slightly lower than those of the CDPC algorithm but superior to other algorithms. For the abalone dataset, the ADPC algorithm achieves the largest \(ARI\) and \(AMI\), while the SNNDPC algorithm achieves the largest \(FMI\). The values of each evaluation index for the DP-CDPC algorithm are lower than those of the CDPC algorithm. The values of each external evaluation index for the DP-ADPC algorithm are slightly lower than those of the ADPC algorithm.