Consensus clustering: Difference between revisions

Revision as of 13:28, 11 April 2022

Revision as of 17:22, 11 April 2022

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#'''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_Cluster_ensembles_for_high_dimensional_clustering_an_empirical_study}} </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">{{citationcite journal needed|date last=JulyFred 2020| first=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|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>

#'''Topchy et al.''': They defined clustering aggregation as a maximum likelihood estimation problem, and they proposed an [[EM algorithm]] for finding the consensus clustering.<ref>Alexander Topchy, Anil K. Jain, William Punch. [http://dataclustering.cse.msu.edu/papers/TPAMI-ClusteringEnsembles.pdf Clustering Ensembles: Models of Consensus and Weak Partitions]. IEEE International Conference on Data Mining, ICDM 03 & SIAM International Conference on Data Mining, SDM 04</ref>