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dc.contributor.authorVogogias, Athanasios
dc.contributor.authorKennedy, Jessie
dc.contributor.authorArchaumbault, Daniel
dc.contributor.authorSmith, Victoria Anne
dc.contributor.authorCurrant, Hannah
dc.contributor.editorTurkay, Cagatay
dc.contributor.editorWan, Tao R.
dc.identifier.citationVogogias , A , Kennedy , J , Archaumbault , D , Smith , V A & Currant , H 2016 , MLCut : exploring Multi-Level Cuts in dendrograms for biological data . in C Turkay & T R Wan (eds) , Computer Graphics and Visual Computing Conference (CGVC) 2016 . Eurographics Association , Computer Graphics & Visual Computing (CGVC) 2016 , Bournemouth , United Kingdom , 15/09/16 .
dc.identifier.otherORCID: /0000-0002-0487-2469/work/32209209
dc.description.abstractChoosing a single similarity threshold for cutting dendrograms is not sufficient for performing hierarchical clustering analysis of heterogeneous data sets. In addition, alternative automated or semi-automated methods that cut dendrograms in multiple levels make assumptions about the data in hand. In an attempt to help the user to find patterns in the data and resolve ambiguities in cluster assignments, we developed MLCut: a tool that provides visual support for exploring dendrograms of heterogeneous data sets in different levels of detail. The interactive exploration of the dendrogram is coordinated with a representation of the original data, shown as parallel coordinates. The tool supports three analysis steps. Firstly, a single-height similarity threshold can be applied using a dynamic slider to identify the main clusters. Secondly, a distinctiveness threshold can be applied using a second dynamic slider to identify “weak-edges” that indicate heterogeneity within clusters. Thirdly, the user can drill-down to further explore the dendrogram structure - always in relation to the original data - and cut the branches of the tree at multiple levels. Interactive drill-down is supported using mouse events such as hovering, pointing and clicking on elements of the dendrogram. Two prototypes of this tool have been developed in collaboration with a group of biologists for analysing their own data sets. We found that enabling the users to cut the tree at multiple levels, while viewing the effect in the original data, isa promising method for clustering which could lead to scientific discoveries.
dc.publisherEurographics Association
dc.relation.ispartofComputer Graphics and Visual Computing Conference (CGVC) 2016en
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectQH301 Biologyen
dc.titleMLCut : exploring Multi-Level Cuts in dendrograms for biological dataen
dc.typeConference itemen
dc.contributor.institutionUniversity of St Andrews. School of Biologyen
dc.contributor.institutionUniversity of St Andrews. Scottish Oceans Instituteen
dc.contributor.institutionUniversity of St Andrews. Institute of Behavioural and Neural Sciencesen
dc.contributor.institutionUniversity of St Andrews. Centre for Research into Ecological & Environmental Modellingen
dc.contributor.institutionUniversity of St Andrews. Centre for Biological Diversityen

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