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Computer Graphics and Visual Computing (CGVC)
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 ofdoi:10.2312/cgvc.20161288 dblp:conf/tpcg/VogogiasKASC16 fatcat:abhkq53dnjfdnnigfrdattly7a