<?xml version="1.0" encoding="UTF-8"?> <article key="pdf/898" mdate="2009-02-25 00:00:00"> <author>Reza Ghaemi and Md. Nasir Sulaiman and Hamidah Ibrahim and Norwati Mustapha</author> <title>A Survey Clustering Ensembles Techniques</title> <pages>365 - 374</pages> <year>2009</year> <volume>3</volume> <number>2</number> <journal>International Journal of Computer and Information Engineering</journal> <ee>https://publications.waset.org/pdf/898</ee> <url>https://publications.waset.org/vol/26</url> <publisher>World Academy of Science, Engineering and Technology</publisher> <abstract>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.</abstract> <index>Open Science Index 26, 2009</index> </article>