Song, Won-Min and Di Matteo, T. and Aste, T. (2012) Hierarchical Information Clustering by Means of Topologically Embedded Graphs. PLoS ONE, 7 (3). e31929. ISSN 1932-6203. (Full text available)
Available under License Creative Commons Attribution.
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.
|Divisions:||Faculties > Science Technology and Medical Studies > School of Physical Sciences > Functional Materials Group|
|Depositing User:||Tomaso Aste|
|Date Deposited:||20 Mar 2012 16:00|
|Last Modified:||28 Mar 2012 08:40|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/29167 (The current URI for this page, for reference purposes)|