Ensemble clustering selection by optimization of accuracy-diversity trade off

The aim of clustering is to group objects which have common features within the group but have dissimilarities with other groups. Clustering algorithms involve finding a common structure without using any label, similarly like other unsupervised methods. Recent studies show that the decision of the ensemble clusters gives more accurate results than any single clustering solution. Besides that, the accuracy and diversity of the ensemble are one of the important factors which effect the overall success of the algorithm. There is a trade off between accuracy and diversity, in other words, you sacrifice one while you increase the performance of the other. On the other hand, the optimum number of clustering solutions is one of the parameters that effect the final result. Recently, finding the best subset of the ensemble clustering solutions by eliminating the redundant solutions has become one of the most challenging problems in the literature. The proposed study here aims to find a best model which optimizes the accuracy and diversity trade off by selecting the best subset of cluster ensemble.