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Clustering ensembles have emerged as a powerful method for improving both the robustness and the stability of unsupervised classification solutions. However, finding a consensus clustering from multiple clusterings is a difficult problem that can be approached from graph-based, combinatorial or statistical perspectives. A consensus scheme via the genetic algorithm based on information theory is proposed...
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