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HIERARCHICAL CLUSTERING PACKAGE SYMBOL
Agglomerate
gives a hierarchical clustering of the elements 
, 
, ....
represents 
with 
in each cluster.
represents 
with 
in each cluster.
Details
- 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 DistanceFunction->Automatic,
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.
