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

TL;DR

This paper introduces an evolutionary approach to optimize the combination of multiple fusion functions in consensus clustering, improving the accuracy and robustness of hierarchical clustering ensembles.

Contribution

It proposes a novel evolutionary fusion function method that enhances consensus clustering by integrating genetic algorithms with hierarchical clustering ensembles.

Findings

Improved accuracy of clustering results compared to traditional methods

Demonstrated robustness across various datasets

Enhanced stability of ensemble clustering

Abstract

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.