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Recent studies show that ensemble methods enhance the stability and robustness of unsupervised learning. These approaches are successfully utilized to construct multiple clustering and combine them into a one representative consensus clustering of an improved quality. The quality of the consensus clustering is directly depended on fusion functions used in combination. In this article, the hierarchical clustering ensemble techniques are extended by introducing a new evolutionary fusion function.arXiv:1805.12270v1 fatcat:md5edh5gcrcbjfvztooe7gu33u