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Dendrogram

Diagram with a treelike structure

Wikipedia contributorsAdapted for Fathomly · View authors
1 min read
Source updated February 26, 2026
Image credit is listed at the end of this article.

A dendrogram is a diagram representing a tree graph. This diagrammatic representation is frequently used in different contexts:

The name dendrogram derives from the two ancient greek words δένδρον (déndron), meaning "tree", and γράμμα (grámma), meaning "drawing, mathematical figure".

01Clustering example

For a clustering example, suppose that five taxa (a to e) have been clustered by UPGMA based on a matrix of genetic distances. The hierarchical clustering dendrogram would show a column of five nodes representing the initial data (here individual taxa), and the remaining nodes represent the clusters to which the data belong, with the arrows representing the distance (dissimilarity). The distance between merged clusters is monotone, increasing with the level of the merger: the height of each node in the plot is proportional to the value of the intergroup dissimilarity between its two daughters (the nodes on the right representing individual observations all plotted at zero height).

Dendrogram output for hierarchical clustering of marine provinces using presence / absence of sponge species.
Dendrogram output for hierarchical clustering of marine provinces using presence / absence of sponge species.
A dendrogram of the Tree of Life. This phylogenetic tree is adapted from Woese et al. rRNA analysis. The vertical line at bottom represents the last universal common ancestor (LUCA).
A dendrogram of the Tree of Life. This phylogenetic tree is adapted from Woese et al. rRNA analysis. The vertical line at bottom represents the last universal common ancestor (LUCA).
Heatmap of RNA-Seq data showing two dendrograms in the left and top margins.
Heatmap of RNA-Seq data showing two dendrograms in the left and top margins.
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Sources and credits

This article is adapted from the Wikipedia article “Dendrogram”, written by its contributors and licensed under CC BY-SA 4.0. Fathomly has changed the layout, removed citation markers, navigation and maintenance notices, and adjusted punctuation. This adapted version is shared under the same license. For references, see the original article.

Images, from Wikimedia Commons:

  • UPGMA Dendrogram Hierarchical.svg by This picture is a work by Emmanuel Douzery. Please credit this with: Picture : Emmanuel Douzery in the immediate vicinity of the picture. If you use one of my works, please email me (account needed) or leave me a short message on my discussion page. It would be greatly appreciated! Do not copy this picture illegally by ignoring the terms of the license below, as it is not in the public domain. If you would like special permission to use, license, or purchase the picture, please contact me to negotiate terms., CC BY-SA 4.0
  • Global-Diversity-of-Sponges-(Porifera)-pone.0035105.s008.tif by Van Soest R, Boury-Esnault N, Vacelet J, Dohrmann M, Erpenbeck D, De Voogd N, Santodomingo N, Vanhoorne B, Kelly M, Hooper J, CC BY 3.0
  • Phylogenetic tree.svg by This vector version: Eric Gaba (Sting, fr:Sting), Cherkash, Public domain
  • Heatmap RNAseqV2 1.png by Eun-Seok Choi, CC BY-SA 4.0

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Related topics

Cladogram

A cladogram is a diagram used in cladistics to show evolutionary relations (common descent) between groups of organisms. Cladograms are a type (subset) of phylogenetic trees that do not normally show evolutionary time but are required to meet specific criteria defined by cladistics.

Distance matrices in phylogeny

Distance matrices are used in phylogeny as non-parametric distance methods and were originally applied to phenetic data using a matrix of pairwise distances. These distances are then reconciled to produce a tree.

Hierarchical clustering

In data mining and statistics, hierarchical clustering is a method of cluster analysis that seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two categories: Agglomerative: Agglomerative clustering, often referred to as a "bottom-up" approach, begins with each data point as an individual cluster.