Method of result aggregation from multiple clustering algorithms
Consensus clustering is a method of aggregating (potentially conflicting) results from multiple clustering algorithms. Also called cluster ensembles[1] 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.[2] 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,[3] even when the number of input clusterings is three.[4] Consensus clustering for unsupervised learning is analogous to ensemble learning in supervised learning.
^Strehl, Alexander; Ghosh, Joydeep (2002). "Cluster ensembles – a knowledge reuse framework for combining multiple partitions" (PDF). Journal on Machine Learning Research (JMLR). 3: 583–617. doi:10.1162/153244303321897735. S2CID 3068944. This paper introduces the problem of combining multiple partitionings of a set of objects into a single consolidated clustering without accessing the features or algorithms that determined these partitionings. We first identify several application scenarios for the resultant 'knowledge reuse' framework that we call cluster ensembles. The cluster ensemble problem is then formalized as a combinatorial optimization problem in terms of shared mutual information
^VEGA-PONS, SANDRO; RUIZ-SHULCLOPER, JOSÉ (1 May 2011). "A Survey of Clustering Ensemble Algorithms". International Journal of Pattern Recognition and Artificial Intelligence. 25 (3): 337–372. doi:10.1142/S0218001411008683. S2CID 4643842.
^Filkov, Vladimir (2003). "Integrating microarray data by consensus clustering". Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence. pp. 418–426. CiteSeerX 10.1.1.116.8271. doi:10.1109/TAI.2003.1250220. ISBN 978-0-7695-2038-4. S2CID 1515525.
^Bonizzoni, Paola; Della Vedova, Gianluca; Dondi, Riccardo; Jiang, Tao (2008). "On the Approximation of Correlation Clustering and Consensus Clustering". Journal of Computer and System Sciences. 74 (5): 671–696. doi:10.1016/j.jcss.2007.06.024.
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