Dendrogram

Dendrogram[{e1,e2,}]

constructs a dendrogram from the hierarchical clustering of the elements e1, e2, .

Dendrogram[{e1v1,e2v2,}]

represents ei with vi in the constructed dendrogram.

Dendrogram[{e1,e2,}{v1,v2,}]

represents ei with vi in the constructed dendrogram.

Dendrogram[label1e1,label2e2,]

represents ei using labels labeli in the constructed dendrogram.

Dendrogram[data,orientation]

constructs an oriented dendrogram according to orientation.

Dendrogram[tree]

constructs the dendrogram corresponding to weighted tree tree.

Details and Options

Examples

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Basic Examples  (4)

Obtain a dendrogram from a list of numbers:

Obtain a dendrogram from a weighted tree:

Obtain a dendrogram from a list of cities and place the labels on the left:

Obtain a cluster hierarchy from a list of Boolean entries:

Scope  (7)

Obtain a dendrogram from a list of colors and display it to the left:

Compare the result with Dendrogram applied to the result of ClusteringTree:

Obtain a dendrogram from a heterogeneous dataset:

Compare it with the dendrogram of the colors:

Generate a sequence of random reals:

Obtain the dendrogram with the labeling given by the rounded reals:

Compute the dendrogram from an Association:

Compare it with the dendrogram of its Values:

Compare it with the dendrogram of its Keys:

Generate a dendrogram from a list of numbers:

Show the axis to compare distances between subclusters:

Generate a dendrogram from a list of vectors:

Display the result using vertical labeling:

Display the result using the ArrayPlot of the vectors as labeling:

Obtain a dendrogram from a list of images:

Options  (6)

AspectRatio  (3)

By default, the ratio of the height to width for the plot is determined automatically:

Make the height the same as the width with AspectRatio1:

Specify the height to width ratio:

ClusterDissimilarityFunction  (1)

Generate a list of random colors:

Obtain a cluster hierarchy from the list using the "Centroid" linkage:

Obtain a cluster hierarchy from the list using the "Single" linkage:

Obtain a cluster hierarchy from the list using a different "ClusterDissimilarityFunction":

DistanceFunction  (1)

Generate a list of random vectors:

Obtain a dendrogram using the automatically chosen DistanceFunction and plot the axis:

Obtain a dendrogram using the EuclideanDistance and compare the values on the axis:

Obtain a dendrogram using a different DistanceFunction:

FeatureExtractor  (1)

Obtain a dendrogram from a list of pictures:

Use a different FeatureExtractor to extract features:

Use the Identity FeatureExtractor to leave the data unchanged:

Applications  (1)

Generate a list of random colors and compute its dendrogram with the distances on the y axis:

Compute the ClusteringTree for the same data by merging clusters that are closer than 0.65:

Compute the Dendrogram of the above graph:

Construct a Manipulate to visualize how clusters merge when the distance threshold increases:

Wolfram Research (2016), Dendrogram, Wolfram Language function, https://reference.wolfram.com/language/ref/Dendrogram.html (updated 2017).

Text

Wolfram Research (2016), Dendrogram, Wolfram Language function, https://reference.wolfram.com/language/ref/Dendrogram.html (updated 2017).

CMS

Wolfram Language. 2016. "Dendrogram." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2017. https://reference.wolfram.com/language/ref/Dendrogram.html.

APA

Wolfram Language. (2016). Dendrogram. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/Dendrogram.html

BibTeX

@misc{reference.wolfram_2023_dendrogram, author="Wolfram Research", title="{Dendrogram}", year="2017", howpublished="\url{https://reference.wolfram.com/language/ref/Dendrogram.html}", note=[Accessed: 03-March-2024 ]}

BibLaTeX

@online{reference.wolfram_2023_dendrogram, organization={Wolfram Research}, title={Dendrogram}, year={2017}, url={https://reference.wolfram.com/language/ref/Dendrogram.html}, note=[Accessed: 03-March-2024 ]}