# ConDPC: Data Connectivity-Based Density Peak Clustering
## 4 Experiments and Results
### Experiments on Real-World Datasets
#### 4.2.2 Results on Real-World Datasets  
Table 5 shows the best clustering index values of the four algorithms on the six datasets. The last column is the average value of the clustering index of each algorithm on the datasets. Figure 11a shows the best F-measure result of each algorithm on each dataset, and Figure 11b shows the average index values of the algorithms on the six datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Source** & **Instances** & **Features** & **Clusters** \\ \hline Iris & [35] & 150 & 4 & 3 \\ Wine & [35] & 178 & 13 & 3 \\ Wpbc & [35] & 198 & 32 & 2 \\ Seeds & [35] & 210 & 7 & 7 \\ Ecoli & [35] & 336 & 7 & 8 \\ Pageblocks & [35] & 5473 & 10 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Information of the real-world datasets.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Algorithm** & & **Aggregation** & **Flame** & **R15** & **Jain** & **Twocircle** & **Halfkernal** & **Halfcircle** & **Threecircle** \\ \hline \multirow{4}{*}{ConDPC} & F-measure & 0.998 & 1 & 0.997 & 1 & 1 & 1 & 1 & 1 \\  & FMI & 0.997 & 1 & 0.993 & 1 & 1 & 1 & 1 & 1 \\  & ARI & 0.996 & 1 & 0.993 & 1 & 1 & 1 & 1 & 1 \\  & _Parameter_ & 3.1 & 2.7 & 3.4 & 3 & 2.7 & 1.5 & 3.8 & 7.2 \\ \hline \multirow{4}{*}{DPC} & F-measure & 0.998 & 1 & 0.997 & 0.910 & 0.610 & 0.819 & 0.943 & 0.586 \\  & FMI & 0.997 & 1 & 0.993 & 0.882 & 0.534 & 0.729 & 0.884 & 0.492 \\  & ARI & 0.996 & 1 & 0.993 & 0.715 & 0.050 & 0.422 & 0.824 & 0.214 \\  & _Parameter_ & 3.1 & 2.7 & 3.4 & 0.3 & 0.2 & 0.1 & 4.2 & 0.2 \\ \hline \multirow{4}{*}{DBSCAN} & F-measure & 0.996 & 0.979 & 0.994 & 0.976 & 1 & 1 & 1 & 1 \\  & FMI & 0.994 & 0.974 & 0.988 & 0.990 & 1 & 1 & 1 & 1 \\  & ARI & 0.992 & 0.944 & 0.987 & 0.976 & 1 & 1 & 1 & 1 \\  & _Parameter_ & 0.08/21 & 0.1/10 & 0.05/30 & 0.08/2 & 0.07/4 & 0.08/5 & 0.08/5 & 0.09/5 \\ \hline \multirow{4}{*}{K-Means} & F-measure & 0.834 & 0.833 & 0.997 & 0.864 & 0.510 & 0.522 & 0.798 & 0.451 \\  & FMI & 0.788 & 0.736 & 0.993 & 0.820 & 0.497 & 0.506 & 0.665 & 0.403 \\  & ARI & 0.730 & 0.453 & 0.993 & 0.577 & \(-0.003\) & 0.002 & 0.488 & 0.053 \\  & _Parameter_ & 7 & 2 & 15 & 2 & 2 & 2 & 3 & 3 \\ \hline \multirow{4}{*}{DPC-DBFN} & F-measure & 0.995 & 0.991 & 0.998 & 0.925 & 0.503 & 0.743 & 0.818 & 0.712 \\  & FMI & 0.994 & 0.985 & 0.997 & 0.906 & 0.497 & 0.674 & 0.687 & 0.715 \\  & ARI & 0.993 & 0.967 & 0.996 & 0.768 & \(-0.003\) & 0.267 & 0.522 & 0.476 \\  & _Parameter_ & 30 & 6 & 39 & 14 & 4 & 7 & 3 & 17 \\ \hline \multirow{4}{*}{McDPC} & F-measure & 0.998 & 1 & 0.997 & 1 & 1 & 0.819 & 0.798 & 1 \\  & FMI & 0.997 & 1 & 0.993 & 1 & 1 & 0.729 & 0.665 & 1 \\ \cline{1-1}  & ARI & 0.996 & 1 & 0.993 & 1 & 1 & 0.422 & 0.488 & 1 \\ \cline{1-1}  & _Parameter_ & 0.1/0.01/ & 0.1/0.01/ & 0.05/0.01/ & 0.3/0.3/ & 0.2/0.5/ & 0.01/0.01/ & 0.3/0.5/ & 0.3/0.5/ \\ \cline{1-1}  & 0.21/1 & 0.4/5 & 0.05/2 & 0.23/5 & 0.08/2 & 0.21/5 & 0.3/2 & 0.08/2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performances on synthetic datasets.  
It can be seen from Table 5 and Figure 11 that the average values of the three indexes corresponding to the ConDPC algorithm are the highest.