MLCut: Exploring Multi-Level Cuts in Dendrograms for Biological Data

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dc.contributor.authorVogogias, Athanasiosen_US
dc.contributor.authorKennedy, Jessieen_US
dc.contributor.authorArchambault, Danielen_US
dc.contributor.authorSmith, V. Anneen_US
dc.contributor.authorCurrant, Hannahen_US
dc.contributor.editorCagatay Turkay and Tao Ruan Wanen_US
dc.date.accessioned2016-09-15T09:05:43Z
dc.date.available2016-09-15T09:05:43Z
dc.date.issued2016
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, is a promising method for clustering which could lead to scientific discoveries.en_US
dc.description.sectionheadersVisualisation Techniques
dc.description.seriesinformationComputer Graphics and Visual Computing (CGVC)
dc.identifier.doi10.2312/cgvc.20161288
dc.identifier.isbn978-3-03868-022-2
dc.identifier.issn-
dc.identifier.pages1-8
dc.identifier.urihttps://doi.org/10.2312/cgvc.20161288
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/cgvc20161288
dc.publisherThe Eurographics Associationen_US
dc.subjectI.3.3 [Computer Graphics]
dc.subjectViewing algorithms
dc.subjectH.3.3 [Information Search and Retrieval]
dc.subjectClustering
dc.subjectInformation filtering
dc.titleMLCut: Exploring Multi-Level Cuts in Dendrograms for Biological Dataen_US
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