Consensus clustering has emerged as an important elaboration of the classical clustering problem. Consensus clustering, also called aggregation of clustering (or partitions), 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. Consensus clustering for unsupervised learning is analogous to ensemble learning in supervised learning.
Read more about Consensus Clustering: Issues With Existing Clustering Techniques, Why Consensus Clustering?, Advantages of Consensus Clustering, Related Work, Hard Ensemble Clustering, Soft Clustering Ensembles, Tunable-tightness Partitions
Famous quotes containing the word consensus:
“No consensus of men can make an error erroneous. We can only find or commit an error, not create it. When we commit an error, we say what was an error already.”
—Josiah Royce (1855–1916)