Consensus clustering: Difference between revisions

Revision as of 13:07, 11 April 2022

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#'''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.{{citation needed|date=July 2020}}

#'''Fred and Jain''': They proposed to use a single linkage algorithm to combine multiple runs of the ''k''-means algorithm.{{citation needed|date=July 2020}}

#'''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>{{citationcite neededjournal|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=JulyFebruary 20202002|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>