Reza Ghaemi and Md. Nasir Sulaiman and Hamidah Ibrahim and Norwati Mustapha
A Survey Clustering Ensembles Techniques
365 - 374
2009
3
2
International Journal of Computer and Information Engineering
https://publications.waset.org/pdf/898
https://publications.waset.org/vol/26
World Academy of Science, Engineering and Technology
The clustering ensembles combine multiple partitions
generated by different clustering algorithms into a single clustering
solution. Clustering ensembles have emerged as a prominent method
for improving robustness, stability and accuracy of unsupervised
classification solutions. So far, many contributions have been done to
find consensus clustering. One of the major problems in clustering
ensembles is the consensus function. In this paper, firstly, we
introduce clustering ensembles, representation of multiple partitions,
its challenges and present taxonomy of combination algorithms.
Secondly, we describe consensus functions in clustering ensembles
including Hypergraph partitioning, Voting approach, Mutual
information, Coassociation based functions and Finite mixture
model, and next explain their advantages, disadvantages and
computational complexity. Finally, we compare the characteristics of
clustering ensembles algorithms such as computational complexity,
robustness, simplicity and accuracy on different datasets in previous
techniques.
Open Science Index 26, 2009