The clustering ensembles contains multiple partitions are divided by different clustering algorithms into a single clustering solutions. Clustering ensembles used for improving robustness, stability, and accuracy of unsupervised classification solutions. The major problem of clustering ensemble is the consensus function. Consensus functions in clustering ensembles including hyper graph partition, mutual information, co-association based functions, voting approach and finite machine. The characteristics of clustering ensembles algorithm are computational complexity, robustness, simplicity and accuracy on different datasets in previous techniques.
M. Mekala, P. Elango
Clustering ensembles, Consensus function, Unsupervised classification.
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|Published in :
||Volume 2 | Issue 4 | July-August - 2016
|Date of Publication
Cite This Article
M. Mekala, P. Elango, "A Survey : Clustering Ensemble Techniques with Consensus Function", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 4, pp.597-601, July-August-2016.
URL : http://ijsrset.com/IJSRSET1624119.php