# Density Peaks Clustering Algorithm Based on a Divergence Distance and Tissue--Like P System
## 4 Experiment
### Experiments on Real-World Datasets  
The UCI dataset is a widely recognized standard test dataset that is frequently used in clustering research. To further prove the performance of TP-DSDPC, we conducted experiments on six UCI datasets. Table 7 indicates the specific information of the datasets. The proposed algorithm is universally applicable to a wide variety of complex datasets, based on the results of the experiments.  
The results of ACC, NMI and ARI are separately presented in Tables 8-10. Bold is used to highlight the best outcomes.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Datasets** & **K-Means** & **DBSCAN** & **DPC** & \
\begin{tabular}{c} **KNN-** \\ **DPC** \\ \end{tabular} & **DGDPC** &
\begin{tabular}{c} **TP-** \\ **DSDPC** \\ \end{tabular} \\ \hline \multirow{2}{*}{Flam} & 0.1904 & 0.2718 & 0.4205 & 0.6251 & 0.7619 & **1.0000** \\  & (0.0281) & (0.0110) & (0.0112) & (0.0246) & (0.0415) & **(0.0000)** \\ \multirow{2}{*}{Jain} & 0.4759 & 0.6014 & 0.5813 & 0.4206 & 0.5381 & **1.0000** \\  & (0.0297) & (0.0157) & (0.0046) & (0.0096) & (0.0429) & **(0.0000)** \\ \multirow{2}{*}{Spiral} & 0.1028 & **1.0000** & 0.2998 & **1.0000** & **1.0000** & **1.0000** \\  & (0.0140) & **(0.0000)** & (0.0576) & **(0.0000)** & **(0.0000)** & **(0.0000)** \\ \multirow{2}{*}{Threecircles} & 0.1358 & 0.9987 & 0.2769 & 0.1137 & **1.0000** & **1.0000** \\  & (0.0208) & (0.0409) & (0.0563) & (0.0451) & **(0.0000)** & **(0.0000)** \\ \multirow{2}{*}{Smile} & 0.6144 & 0.6369 & **1.0000** & **1.0000** & **1.0000** & **1.0000** \\  & (0.0446) & (0.0274) & **(0.0000)** & **(0.0000)** & **(0.0000)** & **(0.0000)** \\ \multirow{2}{*}{Fourlines} & 0.2588 & 0.9973 & **1.0000** & **1.0000** & **1.0000** & **1.0000** \\  & (0.0468) & (0.0177) & **(0.0000)** & **(0.0000)** & **(0.0000)** & **(0.0000)** \\ \multirow{2}{*}{Aggregation} & 0.1028 & 0.0099 & 0.3579 & 0.4899 & **1.0000** & **1.0000** \\  & (0.0181) & (0.0560) & (0.0198) & (0.0761) & **(0.0000)** & **(0.0000)** \\ \multirow{2}{*}{R15} & 0.5108 & 0.4236 & 0.7003 & 0.7140 & 0.9047 & **1.0000** \\  & (0.1235) & (0.0361) & (0.0285) & (0.0419) & (0.0526) & **(0.0000)** \\ \hline \hline \end{tabular}
\end{table}
Table 6: The ARI of the six algorithms on nine synthetic datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Objects** & **Attributes** & **Clusters** \\ \hline Iris & 150 & 4 & 3 \\ Ecoli & 336 & 8 & 8 \\ Zoo & 101 & 18 & 7 \\ Glass & 214 & 10 & 6 \\ Yeast & 1484 & 9 & 10 \\ Seeds & 210 & 7 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The basic information of the six UCI datasets.  
In order to more intuitively observe the ACC, NMI and ARI of six algorithms on these real-world datasets, histograms were applied to represent them, as can be seen separately from Figures 14-16.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
Regarding the ACC, Figure 14 illustrates that the TP-DSDPC algorithm has the best results on the five real-world datasets, except for the Ecoli dataset. The results from the Ecoli dataset are also the second best. In terms of the NMI, Figure 15 demonstrates that the proposed algorithm achieved excellent results on most datasets. At the same time, it is only second to the DPC and DGDPC algorithms on the Zoo and Seeds datasets. In terms of the ARI, as displayed in Figure 16, the proposed algorithm obtained the best results on most datasets, expect for the Iris and Glass datasets.  
Figure 14: The ACC of the six algorithms on six real-world datasets.  
Figure 15: The NMI of the six algorithms on six real-world datasets.