Authors:
Sunandana Banerjee,Deepti Bala Mishra,DOI NO:
https://doi.org/10.26782/jmcms.2025.08.00012Keywords:
Consensus Clustering,Clustering Ensemble,Clustering Techniques,Dual-Weighted Approach,Abstract
This paper presents a novel consensus clustering framework that integrates both cluster-level and clustering-level weighting strategies. Traditional consensus clustering methods either weight the clusters or the base clusterings, but often fail to optimally combine these two strategies. We propose a dual-weighting scheme where weights are assigned to clusters based on internal and external consistency, and to the base clusterings based on their agreement with the ensemble. By applying a combined weight, we ensure that both high-quality clusters and consistent clusterings contribute more to the final consensus. Experimental results on several benchmark datasets demonstrate the superiority of the proposed method over existing clustering ensemble techniques.Refference:
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