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How frequently do clusters occur in hierarchical clustering analysis? A graph theoretical approach to studying ties in proximity

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dc.creatorLeal, Wilmerspa
dc.creatorLlanos, Eugenio J.spa
dc.creatorRestrepo, Guillermospa
dc.creatorSuárez, Carlos F.spa
dc.creatorPatarroyo, Manuel Elkinspa
dc.date.accessioned2020-05-25T23:55:59Z
dc.date.available2020-05-25T23:55:59Z
dc.date.created2016spa
dc.description.abstractBackground: Hierarchical cluster analysis (HCA) is a widely used classificatory technique in many areas of scientific knowledge. Applications usually yield a dendrogram from an HCA run over a given data set, using a grouping algorithm and a similarity measure. However, even when such parameters are fixed, ties in proximity (i.e. two equidistant clusters from a third one) may produce several different dendrograms, having different possible clustering patterns (different classifications). This situation is usually disregarded and conclusions are based on a single result, leading to questions concerning the permanence of clusters in all the resulting dendrograms; this happens, for example, when using HCA for grouping molecular descriptors to select that less similar ones in QSAR studies. Results: Representing dendrograms in graph theoretical terms allowed us to introduce four measures of cluster frequency in a canonical way, and use them to calculate cluster frequencies over the set of all possible dendrograms, taking all ties in proximity into account. A toy example of well separated clusters was used, as well as a set of 1666 molecular descriptors calculated for a group of molecules having hepatotoxic activity to show how our functions may be used for studying the effect of ties in HCA analysis. Such functions were not restricted to the tie case; the possibility of using them to derive cluster stability measurements on arbitrary sets of dendrograms having the same leaves is discussed, e.g. dendrograms from variations of HCA parameters. It was found that ties occurred frequently, some yielding tens of thousands of dendrograms, even for small data sets. Conclusions: Our approach was able to detect trends in clustering patterns by offering a simple way of measuring their frequency, which is often very low. This would imply, that inferences and models based on descriptor classifications (e.g. QSAR) are likely to be biased, thereby requiring an assessment of their reliability. Moreover, any classification of molecular descriptors is likely to be far from unique. Our results highlight the need for evaluating the effect of ties on clustering patterns before classification results can be used accurately. © 2016 Leal et al.eng
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.1186/s13321-016-0114-x
dc.identifier.issn17582946
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/22285
dc.language.isoengspa
dc.publisherBioMed Central Ltd.spa
dc.relation.citationIssueNo. 1
dc.relation.citationTitleJournal of Cheminformatics
dc.relation.citationVolumeVol. 8
dc.relation.ispartofJournal of Cheminformatics, ISSN:17582946, Vol.8, No.1 (2016)spa
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84958102688&doi=10.1186%2fs13321-016-0114-x&partnerID=40&md5=605590ad3b3b3c9de85624edec80f43aspa
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.rights.accesoAbierto (Texto Completo)spa
dc.source.instnameinstname:Universidad del Rosariospa
dc.source.reponamereponame:Repositorio Institucional EdocURspa
dc.subject.keywordCluster frequencyspa
dc.subject.keywordCluster stabilityspa
dc.subject.keywordDendrogramspa
dc.subject.keywordHierarchical cluster analysis (HCA)spa
dc.subject.keywordMolecular descriptorspa
dc.subject.keywordTies in proximityspa
dc.titleHow frequently do clusters occur in hierarchical clustering analysis? A graph theoretical approach to studying ties in proximityspa
dc.typearticleeng
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.type.spaArtículospa
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