## 4 Experimental Results
### Reinforcement Scenario Experiments
#### 4.2.2 Results Discussion
A significant improvement can also be seen in the classification performance metrics after the appointment process, as the percentage of progress in the F1 score reached 19%. Following training once the reinforcement process was complete, a good accuracy was achieved. The data in Table 7 indicate that there is a slight increase in performance, as the percentage purity index improvement is 18% and there is an increase in the Rand index although the DB index remained practically unchanged. The performance of the meta-classifier remained constant in both tables. It can be concluded from both tables that enhancing the algorithm specialised in processing quantitative data with information from algorithms specialised in processing qualitative data, or by using inputs from algorithms specialised in different data altogether, has a significant impact on improving the result quality. This proves the strength of the proposed framework in the reinforcement scenario. The performance of the meta-classifier remains constant because it depends on the results of all the clustering algorithms shown in Table 5. Its performance is therefore unaffected by the improvement in the results of only a single algorithm. Tables 8 and 9 contain the data which describe the performance evaluation of the BMM\({}_{\text{(1)}}\) algorithm following its enhancement by algorithms specialised in processing quantitative data only and by both a combination of quantitative and qualitative algorithms, respectively. As in previous experiments, the performance of the meta-classifier is assessed after the reinforcement process. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Algorithm** & **DB Index** & **Rand Index** & **Purity** & **Sensitivity** & **Precision** & **F1 Score** & **Accuracy** \\ \hline K-means\({}_{\text{cosine (1)}}\) & 0.65664 & 0.60028 & 72.44\% & 75.197\% & 75.049\% & 75.123\% & 72.44\% \\ Meta-classifier & - & - & - & 90.551\% & 82.883\% & 86.547\% & 84.423\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Reinforcement of the K-means\({}_{\text{cosine (1)}}\) algorithm by algorithms specialised in qualitative data processing. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Algorithm** & **DB Index** & **Rand Index** & **Purity** & **Sensitivity** & **Precision** & **F1 Score** & **Accuracy** \\ \hline K-means\({}_{\text{cosine (1)}}\) & 0.62786 & 0.60724 & 73.203\% & 76.378\% & 75.486\% & 75.929\% & 73.203\% \\ Meta-classifier & - & - & - & 90.551\% & 82.883\% & 86.547\% & 84.423\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Reinforcement of the K-means\({}_{\text{cosine (1)}}\) algorithm by both qualitative and quantitative algorithms. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Algorithm** & **DB Index** & **Rand Index** & **Purity** & **Sensitivity** & **Precision** & **F1 Score** & **Accuracy** \\ \hline Bmm\({}_{\text{(1)}}\) & 1.10311 & 0.69652 & 81.373\% & 84.055\% & 82.592\% & 83.317\% & 81.373\% \\ Meta-classifier & - & - & - & 87.598\% & 83.022\% & 85.249\% & 83.224\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Reinforcement of the BMM\({}_{\text{(1)}}\) algorithm by algorithms specialised in quantitative data processing. The data in both tables demonstrate a significant improvement after the reinforcement process, with an improvement in the purity index of 9% and an increase in the Rand index in both cases. In contrast, the DB external validation index deteriorates, as its value has increased from the value in the local step in both tables. There was a significant improvement in the evaluation performance metrics after the reinforcement process, as the percentage of progress in the F1 score reached 15%. A slight decrease in the performance of the meta-classifier model is also evident, with a 1% fall in the F1 score compared to the previous experiment.