ClusteringTree
ClusteringTree[{e1,e2,…}]
constructs a weighted tree from the hierarchical clustering of the elements e1, e2, ….
ClusteringTree[{e1v1,e2v2,…}]
represents ei with vi in the constructed graph.
ClusteringTree[{e1,e2,…}{v1,v2,…}]
represents ei with vi in the constructed graph.
ClusteringTree[label1e1,label2e2…]
represents ei using labels labeli in the constructed graph.
ClusteringTree[data,h]
constructs a weighted tree from the hierarchical clustering of data by joining subclusters at distance less than h.
Details and Options


- The data elements ei can be numbers; numeric lists, matrices, or tensors; lists of Boolean elements; strings or images; geo positions or geographical entities; colors; as well as combinations of these. If the ei are lists, matrices, or tensors, each must have the same dimensions.
- The result from ClusteringTree is a binary weighted tree, where the weight of each vertex indicates the distance between the two subtrees that have that vertex as root:
- ClusteringTree has the same options as Graph, with the following additions and changes:
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ClusterDissimilarityFunction Automatic the clustering linkage algorithm to use DistanceFunction Automatic the distance or dissimilarity to use EdgeStyle GrayLevel[0.65] styles for edges FeatureExtractor Automatic how to extract features from data VertexSize 0 size of vertices - By default, ClusteringTree will preprocess the data automatically unless either a DistanceFunction or a FeatureExtractor is specified.
- ClusterDissimilarityFunction defines the intercluster dissimilarity, given the dissimilarities between member elements.
- Possible settings for ClusterDissimilarityFunction include:
-
"Average" average intercluster dissimilarity "Centroid" distance from cluster centroids "Complete" largest intercluster dissimilarity "Median" distance from cluster medians "Single" smallest intercluster dissimilarity "Ward" Ward's minimum variance dissimilarity "WeightedAverage" weighted average intercluster dissimilarity a pure function - The function f defines a distance from any two clusters.
- The function f needs to be a real-valued function of the DistanceMatrix.
Examples
open allclose allBasic Examples (5)
Scope (8)
Obtain a cluster hierarchy from a list of numbers:
Look at the distance between subclusters by looking at the VertexWeight:
Find the shortest path from the root vertex to the leaf 3.4:
Obtain a cluster hierarchy from a heterogeneous dataset:
Compare it with the cluster hierarchy of the colors:
Generate a list of random colors:
Obtain a cluster hierarchy from the list using the "Centroid" linkage:
Compute the hierarchical clustering from an Association:
Compare it with the hierarchical clustering of its Values:
Compare it with the hierarchical clustering of its Keys:
Obtain a cluster hierarchy by merging clusters at distance less than 0.4:
Change the style and the layout of the ClusteringTree:
Obtain a cluster hierarchy from a list of three-dimensional vectors and label the leaves with the total of the corresponding element:
Compare it with the cluster hierarchy of the total of each vector:
Options (3)
ClusterDissimilarityFunction (1)
DistanceFunction (1)
Generate a list of random vectors:
Obtain a cluster hierarchy using different DistanceFunction:
FeatureExtractor (1)
Obtain a cluster hierarchy from a list of pictures:
Use a different FeatureExtractor to extract features:
Use the Identity FeatureExtractor to leave the data unchanged:
Text
Wolfram Research (2016), ClusteringTree, Wolfram Language function, https://reference.wolfram.com/language/ref/ClusteringTree.html (updated 2017).
CMS
Wolfram Language. 2016. "ClusteringTree." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2017. https://reference.wolfram.com/language/ref/ClusteringTree.html.
APA
Wolfram Language. (2016). ClusteringTree. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/ClusteringTree.html