## 4 Experimental Results
### Parallel Scenario Experiments
#### 4.3.2 Results Discussion  
The performance data for the clustering algorithms after the local step are presented in Table 10, and include six models specialising in quantitative data and three models specialising in qualitative data. There is diversity in the performance of all the algorithms, which achieves diversity in the solutions. The evaluation of the performance data for the quantitative clustering models after collaboration with each other is shown in Table 11. Two models for each algorithm, i.e., K-means, the K-means cosine GMM, k-mode and the BMM, were created in order to achieve diversity. The evaluation of the qualitative clustering models after their collaboration is presented in Table 12, and the assessment of the quantitative and qualitative clustering models after collaboration with each other is shown in Table 13.  
Numerous differences can be observed in the DB internal validation index results. This could be explained by the fact that although the proposed collaborative framework aims to optimise all outcomes, weaker algorithms often impact the outcomes of the best collaborators negatively. However, on average, the collaboration scores of the DB index remain positive, proving the strength of the proposed collaborative framework. The second \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\({}_{(1)}\) & 1.0987 & 0.69652 & 81.373\% & 84.449\% & 82.342\% & 83.382\% & 81.373\% \\ Meta-classifier & - & - & - & 87.598\% & 83.022\% & 85.249\% & 83.224\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Reinforcement of BMM\({}_{(1)}\) algorithm by both quantitative algorithms and qualitative algorithms. point that these experiments highlight is that the proposed collaborative framework does not solve the problem of achieving good results on external indicators, i.e., in this case, the Rand and purity indices, using completely unsupervised clustering algorithms. A lack of external knowledge could explain the poor performance achieved on these indicators. Thus, there is no reason for the collaborative process to converge on the ground truth. In all experiments, the collaborative step of the proposed method takes an average of 8 to 10 iterations before a stable global entropy is reached. This number decreases slightly when there are only two or three collaborators with extremely close solutions at the end of the local step. Still, it remains primarily constant when the number of collaborators or diversity amongst initial solutions increases. The highest accuracy of the meta-classifier, 85%, was obtained when the quantitative algorithms cooperated. However, in the rest of the experiments, almost the same performance, 83%, was attained. The accuracy of the meta-classifier can be enhanced by increasing the number of collaborating members, provided that diversity is achieved in the solutions, because the meta-classifier training data set consists of the results of the collaborating members. Consequently, the more members that there are, the greater the training data set's advantages and thus the better the meta-classifier learns.