Hierarchical information clustering by means of topologically embedded graphs.

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 ana...

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Autores principales:	Won-Min Song, T Di Matteo, Tomaso Aste
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a720d768c28243c4980b470a8ac3213a
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spelling	oai:doaj.org-article:a720d768c28243c4980b470a8ac3213a2021-11-18T07:25:39ZHierarchical information clustering by means of topologically embedded graphs.1932-620310.1371/journal.pone.0031929https://doaj.org/article/a720d768c28243c4980b470a8ac3213a2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22427814/?tool=EBIhttps://doaj.org/toc/1932-6203We 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.Won-Min SongT Di MatteoT Di MatteoTomaso AstePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 3, p e31929 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Won-Min Song T Di Matteo T Di Matteo Tomaso Aste Hierarchical information clustering by means of topologically embedded graphs.
description	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.
format	article
author	Won-Min Song T Di Matteo T Di Matteo Tomaso Aste
author_facet	Won-Min Song T Di Matteo T Di Matteo Tomaso Aste
author_sort	Won-Min Song
title	Hierarchical information clustering by means of topologically embedded graphs.
title_short	Hierarchical information clustering by means of topologically embedded graphs.
title_full	Hierarchical information clustering by means of topologically embedded graphs.
title_fullStr	Hierarchical information clustering by means of topologically embedded graphs.
title_full_unstemmed	Hierarchical information clustering by means of topologically embedded graphs.
title_sort	hierarchical information clustering by means of topologically embedded graphs.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/a720d768c28243c4980b470a8ac3213a
work_keys_str_mv	AT wonminsong hierarchicalinformationclusteringbymeansoftopologicallyembeddedgraphs AT tdimatteo hierarchicalinformationclusteringbymeansoftopologicallyembeddedgraphs AT tdimatteo hierarchicalinformationclusteringbymeansoftopologicallyembeddedgraphs AT tomasoaste hierarchicalinformationclusteringbymeansoftopologicallyembeddedgraphs
_version_	1718423493122457600

Hierarchical information clustering by means of topologically embedded graphs.

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