## 4 Experimental Results
### Reinforcement Scenario Experiments
#### 4.2.2 Results Discussion  
Table 5 shows the clustering algorithm validation indicators and the classification performance measures, which were assessed prior to the collaborations and following the mapping phase, respectively. The performances of clustering algorithms specialised in qualitative data are often higher than those specialised in quantitative data. This does not mean that these algorithms are superior to the other algorithms. However, it does imply that for determining CVD incidence, qualitative variables have a higher weight. Consequently, it is possible to improve the performance of clustering algorithms specialised in quantitative data processing through the collaborative method, which gives them insight into qualitative data clustering, and vice versa. In Table 5, one model of each of the quantitative data algorithm models from each of the qualitative data algorithms, i.e., k-modes, K-means\({}_{\text{cosine (1)}}\) and BMM, as well as two models from each of the k-modes and BMM qualitative data algorithms, are presented. It is evident that the clustering algorithms specialised in qualitative data have a superior performance to those specialised in quantitative data. This does not mean that these algorithms are more effective than the other algorithms. However, it does infer that with respect to recognising the incidence of heart disease, the qualitative variables have a higher weight. Consequently, there is the potential to enhance the performance of clustering algorithms specialised in quantitative data processing through the collaborative method, which gives them insight into qualitative data clustering, and vice versa. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Algorithm** & **DB Index** & **Rand Index** & **Purity** & **Sensitivity** & **Precision** & **F1 Score** & **Accuracy** \\ \hline \multicolumn{7}{c}{Quantitative Clustering Algorithms} \\ K-means\({}_{\text{(1)}}\) & 1.13915 & 0.51827 & 59.695\% & 31.496\% & 87.912\% & 46.377\% & 59.695\% \\ K-means\({}_{\text{cosine (1)}}\) & 0.62323 & 0.50237 & 55.338\% & 54.528\% & 58.936\% & 56.646\% & 53.813\% \\ Gmm\({}_{\text{(1)}}\) & 0.79985 & 0.63859 & 76.362\% & 83.858\% & 75.936\% & 79.701\% & 76.362\% \\ \hline \multicolumn{7}{c}{Qualitative Clustering Algorithms} \\ K-modes\({}_{\text{(1)}}\) & 0.9349 & 0.6103 & 73.529\% & 62.402\% & 85.908\% & 72.292\% & 73.529\% \\ K-modes\({}_{\text{(2)}}\) & 1.22162 & 0.69652 & 81.373\% & 82.48\% & 83.633\% & 83.052\% & 81.373\% \\ Bmm\({}_{\text{(1)}}\) & 0.66657 & 0.59736 & 72.113\% & 55.709\% & 90.127\% & 68.857\% & 72.113\% \\ Bmm\({}_{\text{(2)}}\) & 1.10022 & 0.69652 & 81.373\% & 84.646\% & 82.218\% & 83.414\% & 81.373\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluate of the performance of all algorithms after the local step. It is also clear that the K-means\({}_{\text{cosine (1)}}\) and BMM\({}_{\text{(1)}}\) algorithms are the weakest algorithms specialised in processing quantitative and qualitative data, respectively. The remainder of this subsection will therefore focus on the effectiveness of the collaborative method for improving these algorithms. In Table 6, the evaluation of the performance of the K-means\({}_{\text{cosine (1)}}\) algorithm after enhancing it with the results of algorithms specialised in qualitative data processing only is shown. This is also appraised after enhancing the outcomes of the two algorithms specialised in qualitative data and the algorithms specialised in data quantitation (Table 6). In both tables, the performance of the meta-classifier is evaluated after the reinforcement process. The results in Table 6 indicate that there is a significant improvement after the reinforcement process, as the percentage of progress in the purity index is 17%. In addition, there is a rise in the Rand index and a minimal increase in the DB index. The Rand index is better when it is close to 1, whereas the DB index is not natural and is best when it is smaller.