Hierarchical Cluster Analysis
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Hierarchical cluster analysis starts by assigning the n data objects or samples to n separate clusters each containing one member. At each stage of the clustering, the two closest clusters are merged into one larger cluster, until finally all the units have been formed into a single cluster. This process can be represented by a hierarchical tree whose nodes indicate what merges have occurred.

Method

A number of methods for clustering are available and vary according to the way in which 'closest' is defined at each stage of merging groups. The following possibilities are available:
Singlelinkdefines the similarity between two clusters as the maximum similarity between any two samples in those clusters
Nearest Neighboursynonym for Singlelink
Complete Linkdefines the similarity between two clusters as the minimum similarity between any two samples in those clusters
Furthest Neighboursynonym for Complete Link
Average Linkdefines the similarity between a cluster and two merging clusters as the average of the similarities with each of the original clusters. It therefore replaces two merging clusters by their mean, unweighted by cluster size
Group Averagean average is taken over all the samples in the two merging clusters. Thus, the original clusters are replaced by their mean, weighted by cluster size
Median Sortingcan be thought of in terms of clusters being represented by points in a multidimensional space; when two clusters join, the new cluster is represented by the midpoint of the original cluster points

Similarity Matrix

The data required for the hierarchical cluster analysis needs to be provided as a symmetric matrix giving the similarity between each pair of units.

Form Similarity Matrix

This produces a menu allowing you to form a similarity matrix from a set of variates.

Available Data

This lists similarity matrices that can be used as input for hierarchical cluster analysis.

See Also