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Recent studies show that ensemble methods enhance the stability androbustness of unsupervised learning. These approaches are successfully utilizedto construct multiple clustering and combine them into a one representativeconsensus clustering of an improved quality. The quality of the consensusclustering is directly depended on fusion functions used in combination. Inthis article, the hierarchical clustering ensemble techniques are extended byintroducing 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 geneticalgorithm among different aggregated clustering methods made by differentfusion functions. Putting some popular data sets to empirical study, thequality of the proposed method is compared with regular clustering ensembles.Experimental results demonstrate the accuracy improvement of the aggregatedclustering results.

Optimized Participation of Multiple Fusion Functions in Consensus Creation: An Evolutionary Approach