SYNOPSIS
Hierarchical clustering implements a number of classic agglomorative (i.e. bottom up) hierarchical clustering methods based on.
OPTIONS
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distanceFunction -- Sets the distance function, which measures the distance between two individual. instances (or possibly the distance between an instance and the centroid of a
cluster depending on the Link type).
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linkType -- Sets the method used to measure the distance between two clusters.
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WARD: finds the distance of the change in caused by merging the cluster. The information of a cluster is calculated as the error sum of squares of the
centroids of the cluster and its members.
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MEAN: calculates the mean distance of a merged cluster (akak Group-average agglomerative clustering)
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SINGLE: find single link distance aka minimum link, which is the closest distance between any item in cluster1 and any item in cluster2
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AVERAGE: finds average distance between the elements of the two clusters
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COMPLETE: find complete link distance aka maximum link, which is the largest distance between any item in cluster1 and any item in cluster2
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CENTROID: finds the distance of the centroids of the clusters
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distanceIsBranchLength -- If set to false, the distance between clusters is interpreted as the height of the node linking the clusters. This is appropriate for example for
single link clustering. However, for neighbor joining, the distance is better interpreted as branch length. Set this flag to get the latter interpretation.
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numClusters -- Sets the number of clusters. If a single hierarchy is desired, set this to 1.