  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. In the proposed method,
multiple hierarchical clustering methods are generated via bagging. Thereafter,
the consensus clustering is obtained using the search capability of genetic
algorithm among different aggregated clustering methods made by different
fusion functions. Putting some popular data sets to empirical study, the
quality of the proposed method is compared with regular clustering ensembles.
Experimental results demonstrate the accuracy improvement of the aggregated
clustering results.
