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Given a set of data points, the output is an upside down tree, known as a dendrogram, whose leaves are the data points and whose internal nodes represent nested clusters of various sizes.
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The TOM matrix was then used as input to average linkage hierarchical clustering that results in a clustering tree (dendrogram) whose branches are identified for cutting, depending on their shape using the dynamic tree-cutting algorithm [ 71].
This step results in a clustering tree (dendrogram) whose branches are identified for cutting depending on their shape using the dynamic tree-cutting algorithm [ 47] which has several advantages over the constant height cut-off method including the ability to identify nested clusters.
These successive clustering operations produced a binary clustering tree (dendrogram), whose roots contained all the observations.
For instance, in contrast to the intuitive zooming style adopted by NaviClusterCS, clusterMaker [ 15] support multi-scale navigation via dendrograms, whose parts, when selected by users, are reflected in the network view of Cytoscape.
Hcluster outputs a dendrogram to illustrate the cluster results.
(g) Clustering panel and clustering results displayed as a dendrogram.
It is represented by a dendrogram (tree diagram) (McKenna 2003).
The results are illustrated as a dendrogram in Fig. 5.
This whole hierarchical process is captured as a "Dendrogram".
These coefficients were utilized to construct a dendrogram using the UPGMA.
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CEO of Professional Science Editing for Scientists @ prosciediting.com