11928511

Source:http://linkedlifedata.com/resource/pubmed/id/11928511

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Predicate	Object
rdf:type	pubmed:Citation
lifeskim:mentions	umls-concept:C0025663, umls-concept:C0205360, umls-concept:C0678594, umls-concept:C1511726, umls-concept:C1527178, umls-concept:C1705938
pubmed:dateCreated	2002-4-3
pubmed:abstractText	We present a method for visually and quantitatively assessing the presence of structure in clustered data. The method exploits measurements of the stability of clustering solutions obtained by perturbing the data set. Stability is characterized by the distribution of pairwise similarities between clusterings obtained from sub samples of the data. High pairwise similarities indicate a stable clustering pattern. The method can be used with any clustering algorithm; it provides a means of rationally defining an optimum number of clusters, and can also detect the lack of structure in data. We show results on artificial and microarray data using a hierarchical clustering algorithm.
pubmed:language	eng
pubmed:journal	http://linkedlifedata.com/resource/pubmed/journal/9711271
pubmed:citationSubset	IM
pubmed:status	MEDLINE
pubmed:issn	1793-5091
pubmed:author	pubmed-author:Ben-HurAsaA, pubmed-author:ElisseeffAndreA, pubmed-author:GuyonIsabelleI
pubmed:issnType	Print
pubmed:owner	NLM
pubmed:authorsComplete	Y
pubmed:pagination	6-17
pubmed:dateRevised	2007-9-12
pubmed:meshHeading	pubmed-meshheading:11928511-Algorithms, pubmed-meshheading:11928511-Cluster Analysis, pubmed-meshheading:11928511-Models, Statistical, pubmed-meshheading:11928511-Reproducibility of Results
pubmed:year	2002
pubmed:articleTitle	A stability based method for discovering structure in clustered data.
pubmed:affiliation	BioWulf Technologies LLC, 2030 Addison st. Suite 102, Berkeley, CA 94704, USA.
pubmed:publicationType	Journal Article