Consensus clustering
Consensus clustering is a method of aggregating (potentially conflicting) results from multiple clustering algorithms. Also called cluster ensembles 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. 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, even when the number of input clusterings is three.
- Bot
- enInternetArchiveBot
- Comment
- enConsensus clustering is a method of aggregating (potentially conflicting) results from multiple clustering algorithms. Also called cluster ensembles 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. 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, even when the number of input clusterings is three.
- Date
- enNovember 2019
- Depiction
- FixAttempted
- enyes
- Has abstract
- enConsensus clustering is a method of aggregating (potentially conflicting) results from multiple clustering algorithms. Also called cluster ensembles 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. 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, even when the number of input clusterings is three. Consensus clustering for unsupervised learning is analogous to ensemble learning in supervised learning.
- Hypernym
- Assignment
- Is primary topic of
- Consensus clustering
- Label
- enConsensus clustering
- Link from a Wikipage to an external page
- web.archive.org/web/20060828084525/http:/www.cs.helsinki.fi/u/tsaparas/publications/aggregated-journal.pdf
- glaros.dtc.umn.edu/gkhome/views/metis
- glaros.dtc.umn.edu/gkhome/metis/hmetis/overview
- bioconductor.org/packages/release/bioc/html/SC3.html
- www.siam.org/proceedings/datamining/2009/SDM09_022_wangh.pdf
- Link from a Wikipage to another Wikipage
- Bayesian probability
- Bipartite graph
- Categorical variable
- Category:Cluster analysis
- Clustering algorithm
- Distance
- EM algorithm
- Ensemble learning
- File:PACexplained.png
- Genetic algorithm
- Gibbs sampling
- Heikki Mannila
- Hierarchical clustering
- Hyper-graph
- K-means
- K-means clustering
- Kullback–Leibler divergence
- NP-complete
- Self-organizing map
- Soft clustering
- SameAs
- 4iMfZ
- m.05h2j
- Q5162841
- Subject
- Category:Cluster analysis
- Thumbnail
- WasDerivedFrom
- Consensus clustering?oldid=1114054845&ns=0
- WikiPageLength
- 22682
- Wikipage page ID
- 21542452
- Wikipage revision ID
- 1114054845
- WikiPageUsesTemplate
- Template:Cite conference
- Template:Dead link
