Song, WM;
Di Matteo, T;
Aste, T;
(2012)
Hierarchical information clustering by means of topologically embedded graphs.
PLOS One
, 7
(3)
, Article e31929. 10.1371/journal.pone.0031929.
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Abstract
We introduce a graph-theoretic approach to extract clusters and hierarchies in complex data-sets in an unsupervised and deterministic manner, without the use of any prior information. This is achieved by building topologically embedded networks containing the subset of most significant links and analyzing the network structure. For a planar embedding, this method provides both the intra-cluster hierarchy, which describes the way clusters are composed, and the inter-cluster hierarchy which describes how clusters gather together. We discuss performance, robustness and reliability of this method by first investigating several artificial data-sets, finding that it can outperform significantly other established approaches. Then we show that our method can successfully differentiate meaningful clusters and hierarchies in a variety of real data-sets. In particular, we find that the application to gene expression patterns of lymphoma samples uncovers biologically significant groups of genes which play key-roles in diagnosis, prognosis and treatment of some of the most relevant human lymphoid malignancies.
| Type: | Article |
|---|---|
| Title: | Hierarchical information clustering by means of topologically embedded graphs. |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1371/journal.pone.0031929 |
| Publisher version: | http://dx.doi.org/10.1371/journal.pone.0031929 |
| Language: | English |
| Additional information: | © 2012 Song et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. No current external funding sources for this study. |
| Keywords: | Cluster Analysis, Gene Regulatory Networks, Humans, Information Science, Information Services, Lymphoma, Models, Theoretical |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1360829 |
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