## 5 Comparison of AAIA with Methods in the Literature  
A comparison of the datasets studied in this publication, and the clustering studies using metaheuristic methods is given in Table 3. There are many methods in the literature such as clustering methods. However, the methods given in Table 3 are examples of clustering studies, especially those conducted with metaheuristic methods.  
In addition, in order to make comparisons with studies in the literature, methods that evaluate with accuracy, precision, recall, and F1 metrics should be selected, because if your metrics are different, it will not be correct to compare with other methods.  
If Table 6 is examined carefully, although the proposed AAIA algorithm is a newly proposed algorithm, its experimental results are quite successful when compared with other metaheuristic methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Iris Dataset**} \\ \hline  & Accuracy & Precision & Recall & F1 \\ AAIA & 0.98 & 0.97 & 0.97 & 0.97 \\ [PERSON] et al. [45] & 0.90 & - & - & - \\ [PERSON] et al. [46] & 0.97 & - & - & 0.97 \\ [PERSON] et al. [47] & - & - & - & 0.82 \\ [PERSON] et al. [48] & 0.85 & - & - & - \\ [PERSON] and Gel [49] & 0.78 & - & - & - \\ \hline \multicolumn{4}{c}{Wisconsin Breast Cancer Original Dataset} \\ \hline AAIA & 0.92 & 0.92 & 0.93 & 0.915 \\ [PERSON] et al. [45] & 0.70 & - & - & - \\ [PERSON] [50] & 0.97 & - & - & - \\ [PERSON] et al. [51] & 0.92 & - & - & - \\ [PERSON] et al. [52] & 0.95 & - & - & - \\ \hline \multicolumn{4}{c}{Occupancy Detection Dataset} \\ \hline AAIA & 0.98 & 0.98 & 0.98 & 0.97 \\ [PERSON] et al. [53] & 0,96 & - & - & - \\ [PERSON] et al. [54] & - & 0.78 & 0.62 & 0.85 \\ [PERSON] [49] & 0.77 & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of AAIA with methods in literature.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Iris Plant Dataset**} \\ \hline  & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline
**Iris Setosa** & 1 & 1 & 1 & 1 \\
**Iris Versicolor** & 0.97 & 0.91 & 1 & 0.96 \\
**Iris Virginica** & 0.97 & 1 & 0.9 & 0.95 \\ \hline
**Average** & **0.98** & **0.97** & **0.97** & **0.97** \\ \hline \multicolumn{4}{c}{**Wisconsin Breast Cancer Dataset**} \\ \hline
**Benign** & 0.92 & 0.84 & 1 & 0.91 \\
**Malignant** & 0.92 & 1 & 086 & 0.92 \\ \hline
**Average** & **0.92** & **0.92** & **0.93** & **0.915** \\ \hline \multicolumn{4}{c}{**Occupancy Detection Dataset**} \\ \hline
**Not Occupied** & 0.95 & 0.9 & 1 & 0.95 \\
**Occupied** & 0.95 & 1 & 0.91 & 0.95 \\ \hline
**Average** & **0.95** & **0.95** & **0.96** & **0.95** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance metrics of datasets.  
Figure 12 is the Q-Q plot graphs of the breast cancer original dataset. Separate graphs are given for each attribute. Because there are too many discrete values, when plotted on the same graph, the points overlap. Therefore, the graph looks meaningless. Therefore, it is plotted separately for each attribute. If Figures 11-13 are examined carefully, it is seen that there are many data points separated from the 45-degree axis line for each dataset. These points are meaningful points to separate the clusters from each other. If all the points are distributed around the axis, the effects of the attributes will not emerge. So, it is seen that there are attribute distributions that can make this distinction in all three datasets. It will be advantageous to take this issue into consideration in algorithms that use the distance-based clustering approach. Otherwise, when choosing the clustering method, grid-based or density-based clustering methods can be selected instead of distance-based. This clustering will affect performance.