Consensus clustering

<p>Consensus clustering is a method of aggregating (potentially conflicting) results from multiple <a href="/facts/Clustering_algorithm/HUeDnWPt">clustering algorithms</a>. Also called cluster ensembles or aggregation of clustering (or partitions), it refers to the situation in which a number of different (input) clusterings have been obtained for a particular dataset and it is desired to find a single (consensus) clustering which is a better fit in some sense than the existing clusterings. Consensus clustering is thus the problem of reconciling clustering information about the same data set coming from different sources or from different runs of the same algorithm. When cast as an optimization problem, consensus clustering is known as median partition, and has been shown to be <a href="/facts/NP-complete/NFPv56DU">NP-complete</a>, even when the number of input clusterings is three. Consensus clustering for unsupervised learning is analogous to <a href="/facts/Ensemble_learning/GvFsd3Qc">ensemble learning</a> in supervised learning.
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Consensus clustering open-in-new

Consensus clustering