# ConDPC: Data Connectivity-Based Density Peak Clustering
## 4 Experiments and Results
### Experimental Discussion  
ConDPC follows the input parameter settings of the original DPC. In the actual experiments, we set the range and step size of the input parameters of the two algorithms to be the same. We set the range of the input parameter _percent_ to [0.1, 9] and the step size to 0.1, and select the best result from the corresponding results.  
In the experiments of artificial datasets in Section 4.1, the value of each clustering index of the DPC algorithm is the highest or equal to the highest. DPC has achieved good  
Figure 11: (**a**) F-measure of each algorithm on each dataset; (**b**) The average index values of each algorithm on the six datasets.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Algorithm** & & **Iris** & **Wine** & **Wpbc** & **Seeds** & **Ecoli** & **Pageblocks** & _Average_ \\ \hline \multirow{4}{*}{ConDPC} & F-measure & 0.967 & 0.879 & 0.673 & 0.913 & 0.444 & 0.322 & 0.700 \\  & FMI & 0.935 & 0.775 & 0.762 & 0.844 & 0.777 & 0.926 & 0.837 \\  & ARI & 0.904 & 0.660 & 0.244 & 0.767 & 0.692 & 0.498 & 0.628 \\  & _Parameter_ & 2.5 & 0.8 & 0.4 & 0.7 & 0.6 & 2.7 & \\ \hline \multirow{4}{*}{DPC} & F-measure & 0.960 & 0.885 & 0.661 & 0.913 & 0.259 & 0.287 & 0.661 \\  & FMI & 0.923 & 0.783 & 0.760 & 0.844 & 0.506 & 0.908 & 0.787 \\  & ARI & 0.886 & 0.672 & 0.227 & 0.767 & 0.349 & 0.387 & 0.548 \\  & _Parameter_ & 0.2 & 2.0 & 7.4 & 0.7 & 0.4 & 8 & \\ \hline \multirow{4}{*}{DBSCAN} & F-measure & 0.587 & 0.526 & 0.439 & 0.543 & 0.298 & 0.293 & 0.448 \\  & FMI & 0.753 & 0.718 & 0.563 & 0.694 & 0.763 & 0.848 & 0.723 \\  & ARI & 0.625 & 0.538 & 0.034 & 0.524 & 0.669 & 0.392 & 0.464 \\  & _Parameter_ & 0.13/9 & 0.51/23 & 0.64/4 & 0.3/35 & 0.23/30 & 0.08/33 & \\ \hline \multirow{4}{*}{K-Means} & F-measure & 0.885 & 0.949 & 0.555 & 0.891 & 0.543 & 0.263 & 0.681 \\  & FMI & 0.811 & 0.903 & 0.579 & 0.803 & 0.559 & 0.583 & 0.706 \\  & ARI & 0.716 & 0.854 & 0.032 & 0.705 & 0.425 & 0.108 & 0.473 \\  & _Parameter_ & 3 & 3 & 2 & 3 & 8 & 5 & \\ \hline \multirow{4}{*}{DPC-DBFN} & F-measure & 0.967 & 0.945 & 0.489 & 0.896 & 0.530 & 0.358 & 0.698 \\  & FMI & 0.935 & 0.888 & 0.721 & 0.809 & 0.755 & 0.914 & 0.837 \\  & ARI & 0.904 & 0.832 & 0.004 & 0.715 & 0.658 & 0.254 & 0.561 \\  & _Parameter_ & 5 & 3 & 19 & 4 & 3 & 39 & \\ \hline \multirow{4}{*}{McDPC} & F-measure & 0.883 & 0.625 & 0.452 & 0.903 & 0.480 & 0.440 & 0.631 \\  & FMI & 0.816 & 0.680 & 0.787 & 0.826 & 0.822 & 0.928 & 0.810 \\ \cline{1-1}  & ARI & 0.720 & 0.439 & 0.008 & 0.742 & 0.739 & 0.524 & 0.529 \\ \cline{1-1}  & _Parameter_ & 0.1/0.1/ & 0.2/0.3/ & 0.2/0.52/ & 0.02/0.02/ & 0.02/0.01/ & 0.01/0.01/ & 0.529 \\ \cline{1-1} \cline{2-10}  & _Parameter_ & 0.34/1 & 0.6/1 & 0.3/0.3 & 0.3/0.3 & 0.3/0.5 & 0.33/1 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performances on real-world datasets.  
clustering results on the Aggregation, Flame, and R15 datasets. The parameter settings of ConDPC on these datasets are completely the same as those of DPC. In fact, the clustering centers, neighbor assignment, and final results obtained by ConDPC are also the same as those of DPC. ConDPC retains the advantage of good clustering effects of DPC on such datasets. DPC performs poorly on the other five datasets while ConDPC still achieves good clustering results.  
In the experiments of real-world datasets in Section 4.2, it can be seen from the clustering index results that ConDPC obtains better clustering results than DPC does on most datasets. The average values of the three indexes of DPC are the highest, corresponding to the best comprehensive evaluation of the clustering results.