HIERARCHICAL CLUSTERING PACKAGE SYMBOL

# Agglomerate

gives a hierarchical clustering of the elements , , ....

represents with in each cluster.

represents with in each cluster.

## DetailsDetails

• To use , you first need to load the Hierarchical Clustering Package using Needs["HierarchicalClustering`"].
• gives a Cluster object.
• The cluster hierarchy may be viewed using DendrogramPlot.
• The data elements can be numbers; numeric lists, matrices, or tensors; lists of Boolean elements; or strings. If the are lists, matrices, or tensors, each must have the same dimensions.
• The following options can be given:
•  DistanceFunction Automatic the distance or dissimilarity measure to use Linkage Automatic the clustering linkage algorithm to use
• With the default setting , uses SquaredEuclideanDistance for numeric data, JaccardDissimilarity for Boolean data, and EditDistance for string data.
• The setting for DistanceFunction can be any distance or dissimilarity function or a pure function f defining a distance between two values.
• defines the intercluster dissimilarity, given the dissimilarities between member elements.
• Possible settings for the Linkage option include:
•  "Single" smallest intercluster dissimilarity "Average" average intercluster dissimilarity "Complete" largest intercluster dissimilarity "WeightedAverage" weighted average intercluster dissimilarity "Centroid" distance from cluster centroids "Median" distance from cluster medians "Ward" Ward's minimum variance dissimilarity f a pure function
• The function f defines a distance from a cluster k to the new cluster formed by fusing clusters i and j.
• The arguments supplied to f are , , , , , and , where d is the distance between clusters and n is the number of elements in a cluster.

## ExamplesExamplesopen allclose all

### Basic Examples (1)Basic Examples (1)

Obtain a cluster hierarchy from a list of numbers:

 Out[2]=