## 4 Experimental Results and Analysis  
### Experimental Environment and Dataset  
In this paper, python3.8.3 is used to verify the validity of the proposed algorithm. The experimental environment is Windows10, Intel(r)Core (TM) i3-7130U CPU, 8.00 GB memory, 64-bit operating system. Several synthetic datasets and UCI datasets are used as experimental datasets, and the characteristics of these datasets are provided in Table 3.  
The datasets flame, spiral, compound, aggregation, R15, and D31 are all two-dimensional composite datasets commonly employed for algorithm testing, and these datasets exhibit different density distributions and shapes.  
The datasets seeds, ecoli, movement, dermatology, banknote, and abalone are all real-world datasets obtained from the UCI Machine Learning Repository. The seeds dataset describes the grain characteristics of three wheat varieties, namely, Kama, Rosa, and Canadian. Each wheat variety contains 70 samples, and each sample contains 7 attributes such as grain area, perimeter, tightness, kernel length, kernel width, asymmetry index, and core groove length. The ecoli dataset is regarding protein localization sites, and contains 336 samples, with each sample containing 7 features divided into 8 categories. The movement dataset comprises 15 classes, each of which contains 24 instances, and each sample includes 46 features. The dermatology dataset is a classified dataset for diagnosing skin diseases: it includes 366 samples, with each containing 33 attributes, grouped into 6 categories. The banknote dataset contains 1372 samples, divided into two categories--genuine banknote and counterfeit banknote. Each sample contains four features extracted from the images used to evaluate the banknote authentication process through wavelet transform  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Attributes** & **Size** & **Clusters** & **Sources** \\ \hline flame & 2 & 240 & 2 & Synthetic \\ spiral & 2 & 312 & 3 & Synthetic \\ compound & 2 & 399 & 6 & Synthetic \\ aggregation & 2 & 788 & 7 & Synthetic \\ R15 & 2 & 600 & 15 & Synthetic \\ D31 & 2 & 3100 & 31 & Synthetic \\ seeds & 7 & 210 & 3 & UCI \\ ecoli & 7 & 336 & 8 & UCI \\ movement & 90 & 360 & 15 & UCI \\ dermatology & 33 & 366 & 6 & UCI \\ banknote & 4 & 1372 & 2 & UCI \\ abalone & 8 & 4177 & 3 & UCI \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental datasets.  
tools. The abalone dataset measures the age of abalone: it contains 4177 samples and is divided into 3 categories by measuring 8 characteristics of each sample.  
To evaluate the effectiveness of the DP-CDPC algorithm, several comparison experiments are conducted. Firstly, different methods for measuring the similarity of UCI datasets are compared. Secondly, the CDPC algorithm is compared with the ADPC [29], K-means [8], DBSCAN [9], and SNNDPC [37] algorithms. Thirdly, the DP-CDPC algorithm is compared with the CDPC and DP-ADPC. Lastly, the clustering effects of different privacy budgets are compared. The Fowlkes and Mallows index [38] (\(FMI\)), Adjusted Rand index [39] (\(ARI\)), and Adjusted Mutual Information index [39] (\(AMI\)) are used in these experiments to assess the clustering performance of the proposed algorithm.