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Seo, J. and Shneiderman, B. (2003) Understanding Hierarchical Clustering Results by Interactive Exploration of Dendrograms: A Case Study with Genomic Microarray Data. IEEE Computer, 35, 1-15.

has been cited by the following article:

• TITLE: Resizable, Rescalable and Free-Style Visualization of Hierarchical Clustering and Bioinformatics Analysis

  AUTHORS: Ruming Li

  KEYWORDS: Hierarchical Clustering, Clustering Visualization, Dendrogram Drawing, Tree Drawing, Resizable and Rescalable, Free-Style Visualization

  JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.8 No.4, September 29, 2020

  ABSTRACT: Graphical representation of hierarchical clustering results is of final importance in hierarchical cluster analysis of data. Unfortunately, almost all mathematical or statistical software may have a weak capability of showcasing such clustering results. Particularly, most of clustering results or trees drawn cannot be represented in a dendrogram with a resizable, rescalable and free-style fashion. With the “dynamic” drawing instead of “static” one, this research works around these weak functionalities that restrict visualization of clustering results in an arbitrary manner. It introduces an algorithmic solution to these functionalities, which adopts seamless pixel rearrangements to be able to resize and rescale dendrograms or tree diagrams. The results showed that the algorithm developed makes clustering outcome representation a really free visualization of hierarchical clustering and bioinformatics analysis. Especially, it possesses features of selectively visualizing and/or saving results in a specific size, scale and style (different views).