Consensus clustering: Difference between revisions

Revision as of 15:36, 28 November 2024

Latest revision as of 05:21, 11 March 2025

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{{Short description|Method of result aggregation from multiple clustering algorithms}}

'''Consensus clustering''' is a method of aggregating (potentially conflicting) results from multiple [[clustering algorithm]]s. Also called '''cluster ensembles'''<ref name=StrehlEnsembles>{{cite journal|last1=Strehl|first1=Alexander|authorlink1=Alexander Strehl|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|doi=10.1162/153244303321897735|s2cid=3068944 |quote=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]]}}</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=Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence|chapter=Integrating microarray data by consensus clustering|year=2003|pages=418–426|doi=10.1109/TAI.2003.1250220|isbn=978-0-7695-2038-4|citeseerx=10.1.1.116.8271|s2cid=1515525}}</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|doi-access=free}}</ref> Consensus clustering for unsupervised learning is analogous to [[ensemble learning]] in supervised learning.

==Issues with existing clustering techniques==

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==Related work==

#'''Clustering ensemble (Strehl and Ghosh)''': They considered various formulations for the problem, most of which reduce the problem to a [[hyper-graph]] partitioning problem. In one of their formulations they considered the same graph as in the correlation clustering problem. The solution they proposed is to compute the best ''k''-partition of the graph, which does not take into account the penalty for merging two nodes that are far apart.<ref name=StrehlEnsembles/>

#'''Clustering aggregation (Fern and Brodley)''': They applied the clustering aggregation idea to a collection of [[soft clustering]]s they obtained by random projections. They used an agglomerative algorithm and did not penalize for merging dissimilar nodes.<ref>{{cite journal|author1=Fern, Xiaoli |author2= Brodley, Carla|year=2004|title=Cluster ensembles for high dimensional clustering: an empirical study|journal=J Mach Learn Res|volume=22|url=https://www.researchgate.net/publication/228476517}} </ref>

#'''Fred and Jain''': They proposed to use a single linkage algorithm to combine multiple runs of the ''k''-means algorithm.<ref name="Fred Jain 2005 pp. 835–850">{{cite journal | last1=Fred | first1=Ana L.N. | last2=Jain | first2=Anil K. | title=Combining multiple clusterings using evidence accumulation | journal=IEEE Transactions on Pattern Analysis and Machine Intelligence | publisher=Institute of Electrical and Electronics Engineers (IEEE) | volume=27 | issue=6 | year=2005 | issn=0162-8828 | doi=10.1109/tpami.2005.113 | pages=835–850|pmid= 15943417| s2cid=10316033 |url=http://dataclustering.cse.msu.edu/papers/TPAMI-0239-0504.R1.pdf}}</ref>

#'''Dana Cristofor and Dan Simovici''': They observed the connection between clustering aggregation and clustering of [[categorical variable|categorical data]]. They proposed information theoretic distance measures, and they propose [[genetic algorithm]]s for finding the best aggregation solution.<ref>{{cite journal|author=Dana Cristofor, Dan Simovici|title=Finding Median Partitions Using Information-Theoretical-Based Genetic Algorithms|journal=Journal of Universal Computer Science|volume=8|issue=2|pages=153–172|url=https://www.jucs.org/jucs_8_2/finding_median_partitions_using/Cristofor_D.pdf|date=February 2002|doi=10.3217/jucs-008-02-0153}}</ref>