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Briefings in Bioinformatics Advance Access originally published online on February 24, 2009
Briefings in Bioinformatics 2009 10(3):297-314; doi:10.1093/bib/bbn058
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© The Author 2009. Published by Oxford University Press. For Permissions, please email: journals.permissions@oxfordjournals.org

A roadmap of clustering algorithms: finding a match for a biomedical application

Bill Andreopoulos, Aijun An, Xiaogang Wang and Michael Schroeder

Corresponding author. Bill Andreopoulos. E-mail: williama{at}biotec.tu-dresden.de

Clustering is ubiquitously applied in bioinformatics with hierarchical clustering and k-means partitioning being the most popular methods. Numerous improvements of these two clustering methods have been introduced, as well as completely different approaches such as grid-based, density-based and model-based clustering. For improved bioinformatics analysis of data, it is important to match clusterings to the requirements of a biomedical application. In this article, we present a set of desirable clustering features that are used as evaluation criteria for clustering algorithms. We review 40 different clustering algorithms of all approaches and datatypes. We compare algorithms on the basis of desirable clustering features, and outline algorithms' benefits and drawbacks as a basis for matching them to biomedical applications.

Keywords: clustering, protein, gene expression, interactome, network, bioinformatics

Submitted: August 28, 2008. Received (in revised form): December 12, 2008.


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