## 5 Conclusions
\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 \multicolumn{8}{c}{Quantitative Clustering Algorithms} \\ \hline K-means\({}_{(1)}\) & 1.12136 & 0.52321 & 60.893\% & 34.252\% & 87.437\% & 49.222\% & 60.893\% \\ \hline K-means\({}_{(2)}\) & 1.11817 & 0.52181 & 60.566\% & 33.465\% & 87.629\% & 48.434\% & 60.566\% \\ \hline K-means\({}_{(1)}\) & 0.77607 & 0.59833 & 72.222\% & 73.031\% & 75.869\% & 74.423\% & 72.222\% \\ \hline K-means\({}_{\text{cosine}\,(2)}\) & 0.69197 & 0.60523 & 72.985\% & 74.409\% & 76.21\% & 75.299\% & 72.985\% \\ \hline Gmm\({}_{(1)}\) & 1.16055 & 0.65273 & 77.669\% & 81.102\% & 79.079\% & 80.078\% & 77.669\% \\ \hline Gmm\({}_{(2)}\) & 1.14702 & 0.56893 & 68.627\% & 50.394\% & 87.671\% & 64.0\% & 68.627\% \\ \hline \multicolumn{8}{c}{Qualitative Clustering Algorithms} \\ \hline Bmm\({}_{(1)}\) & 1.10022 & 0.69652 & 81.373\% & 84.646\% & 82.218\% & 83.414\% & 81.373\% \\ \hline Bmm\({}_{(2)}\) & 1.10373 & 0.70342 & 81.917\% & 86.22\% & 82.022\% & 84,069\% & 81.917\% \\ \hline Bmm\({}_{(3)}\) & 2.5694 & 0.56414 & 67.974\% & 96.26\% & 64.005\% & 76.887\% & 67.974\% \\ \hline Meta-classifier & - & - & - & 92.323\% & 81.282\% & 86.451\% & 83.987\% \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Evaluation of all quantitative and qualitative algorithms after collaboration with each other. achieved superior results to the best algorithms generated by the local phase. Moreover, the concept of the best result in unsupervised learning is highly dependent on the index considered; it is impossible to know in advance which algorithm will give the best results. In most cases, the presented approach therefore remains valid. Given the issue of strong and weak collaborators, future implementations of this work will focus on improving the collaborative process by balancing the influence of collaborators on each other based on quality and diversity criteria, as well as incorporating external knowledge into the collaborative approach. The objective will be to reduce instances of negative collaboration. The proposed algorithm can be used in other applications in several fields such as health and engineering, as well as in the method of ablation by only using two cluster algorithms. Formal analysis, A.-A.-S.; Methodology, N.Z. ; Software, A.A.-S.; Supervision, N.Z. ; Writing--original draft, A.A.-S.; Writing--review and editing, M.M.K. and N.Z. All authors have read and agreed to the published version of the manuscript. This work was funded by the University of Jeddah, Jeddah, Saudi Arabia, under grant no. UJ-23-FR-41. Therefore, the authors thank the University of Jeddah for its technical and financial support. Not applicable. Data available in a publicly accessible repository. The data presented in this study are openly available in repository UCI Machine Learning Repository at [[https://www.kaggle.com/fedesoriano/heart-failure-prediction](https://www.kaggle.com/fedesoriano/heart-failure-prediction)]([https://www.kaggle.com/fedesoriano/heart-failure-prediction](https://www.kaggle.com/fedesoriano/heart-failure-prediction)), reference number [4]. The authors declare no conflict of interest.