## 4 Experimental Results  
### Results of Datasets  
The iris plant dataset, Wisconsin breast cancer dataset, and occupancy detection dataset [42; 43; 44] used in this study are datasets frequently used in data clustering studies. There are 150 samples in total in the iris plant dataset. Each sample contains four data. In the study, 30 samples from each cluster were used for training the algorithm and 20 samples were used for testing. There are 685 samples in total in the Wisconsin breast cancer dataset. Each sample contains nine data. In the study, 585 samples from each cluster were used for training the algorithm and 100 samples were used for testing. There are 8143 samples in total in the Wisconsin breast cancer dataset. Each sample contains five data. In the study,  
Figure 5: General workflow study. 7943 samples from each cluster were used for training the algorithm and 200 samples were used for testing. Table 1 shows the V, best and worst values obtained for datasets. The matrix indicated as best in Table 1 is the value that can perform the clustering process best. The best and worst matrices represent the equivalents of the positive afterimage and negative afterimage values in the afterimage algorithm. They are used to calculate the new values in Equation (10). Figure 6 shows the confusion matrix for datasets. Figure 6 shows the confusion matrices of all three datasets. As can be seen from the figure, there are three clusters for the iris plant dataset. Only for one cluster, 2 out of 20 values are clustered incorrectly. There are two clusters for the Wisconsin breast cancer original dataset. Only for one cluster, 8 out of 50 values are clustered incorrectly. There are two clusters for the occupancy detection dataset. Only for one cluster, 10 out of 100 values are clustered incorrectly. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Obtained Values for Iris Plant Dataset**} \\ \hline Best & 2.9800 & 3.3094 & 1.0343 & 0.0543 \\ Worst & 2.2846 & \(1.6018\times 10^{306}\) & 4.0205 \(\times\) 10\({}^{305}\) & 0.0440 \\ V & & 0.8707 & & \\ \hline \multicolumn{5}{c}{Obtained values for Wisconsin Breast Cancer Dataset} \\ \hline Best & 3.2452 & 0.4773 & 0.3837 & 0.6072 & 0.7001 \\  & 0.5530 & 1.4196 & 0.3803 & 0.4494 \\ Worst & \(2.4528\times 10^{304}\) & 0.2938 & 0.2169 & 0.4762 & 0.4199 \\  & 0.3523 & \(2.5194\times 10^{305}\) & 0.3 & \(4.4899\times 10^{305}\) & \\ V & & 0.9685 & & \\ \hline \hline \multicolumn{5}{c}{Obtained values for Occupancy Detection Dataset} \\ \hline Best & 0.5102 & 0.5625 & 0.4983 & 2.9965 & 1.6872 \\ Worst & 0.3088 & 0.3840 & \(2.6569\times 10^{304}\) & 1.6602 & 1.4436 \\ V & & 0.9947 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental results of datasets. Figure 6: (**a**) Confusion matrix results of iris plant dataset, (**b**) confusion matrix results of Wisconsin breast cancer dataset, (**c**) confusion matrix results of occupancy detection dataset. If Table 4 is examined carefully, the clustering range for the not occupied cluster is between 466.8480 and 841.6300. The clustering range for the occupied cluster is between 926.3340 and 2518.8030. In this case, 10 values are clustered incorrectly for the not occupied cluster. Figures 7-9 show graphical representations of clustering intervals for the iris plant dataset, Wisconsin breast cancer original dataset, and occupancy detection dataset. If the figures are examined carefully, it will be seen that the values detected as errors for the clustering intervals are marked. Two points are marked in Figure 2, eight points in Figure 3, and ten points in Figure 4. These points are the points where there are error values for three datasets. The clustering interval for these remained within the range of another cluster. Therefore, an error occurred at these points. Table 5 shows the performance metrics of the datasets.