A Survey : Clustering Ensemble Techniques with Consensus Function

Authors(2) :-M. Mekala, P. Elango

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.

Authors and Affiliations

M. Mekala
Department of Computer Science, Gobi Arts & Science College, Gobichettipalayam, Erode, Tamil Nadu, India
P. Elango
Department of Computer Science, Gobi Arts & Science College, Gobichettipalayam, Erode, Tamil Nadu, India

Clustering ensembles, Consensus function, Unsupervised classification.

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Publication Details

Published in : Volume 2 | Issue 4 | July-August 2016
Date of Publication : 2016-08-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 597-601
Manuscript Number : IJSRSET1624119
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

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.
Journal URL : http://ijsrset.com/IJSRSET1624119

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