Research Output

MLCut: exploring multi-level cuts in dendrograms for biological data

  Choosing 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

  • Date:

    15 September 2016

  • Publication Status:


  • DOI:


  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    006.6 Computer graphics


Vogogias, A., Kennedy, J., Archambault, D., Anne Smith, V., & Currant, H. (2016). MLCut: exploring multi-level cuts in dendrograms for biological data. In C. Turkay, & T. Ruan Wan (Eds.), Computer Graphics and Visual Computing (CGVC)doi:10.2312/cgvc.20161288



hierarchical; clustering; computer graphics; viewing algorithms; information search and retrieval; information

Monthly Views:

Available Documents