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'''Consensus clustering''' is ana important elaborationmethod of traditionalaggregating [[cluster(potentially analysis]].conflicting) Consensusresults from multiple clustering,alsoalgorithms. Also called '''cluster ensembles'''<ref name=StrehlEnsembles>{{cite journal|last=Strehl|first=Alexander|author2=Ghosh, Joydeep|title=Cluster ensembles – a knowledge reuse framework for combining multiple partitions|journal=Journal on Machine Learning Research (JMLR)|date=2002|volume=3|pages=583–617|url=http://www.jmlr.org/papers/volume3/strehl02a/strehl02a.pdf}}</ref> 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.<ref name=RuizSurvey2011>{{cite journal|last=VEGA-PONS|first=SANDRO|author2=RUIZ-SHULCLOPER, JOSÉ|s2cid=4643842|journal=International Journal of Pattern Recognition and Artificial Intelligence|date=1 May 2011|volume=25|issue=3|pages=337–372|doi=10.1142/S0218001411008683|title=A Survey of Clustering Ensemble Algorithms}}</ref> 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 [[NP-complete]],<ref name=Filkov2003>{{cite book|last=Filkov|first=Vladimir|title=Integrating microarray data by consensus clustering|journal=In Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence.|year=2003|pages=418–426|doi=10.1109/TAI.2003.1250220|isbn=978-0-7695-2038-4|citeseerx=10.1.1.116.8271}}</ref> even when the number of input clusterings is three.<ref name=Bonizzoni2008>{{cite journal|last=Bonizzoni|first=Paola|author2=Della Vedova, Gianluca| author3= Dondi, Riccardo| author4= Jiang, Tao| title=On the Approximation of Correlation Clustering and Consensus Clustering|journal=Journal of Computer and System Sciences|volume=74|number=5|year=2008|pages=671–696|doi=10.1016/j.jcss.2007.06.024}}</ref> Consensus clustering for unsupervised learning is analogous to [[ensemble learning]] in supervised learning.
