### Function Reference: `cluster`

statistics: T = cluster (Z, "Cutoff", C)
statistics: T = cluster (Z, "Cutoff", C, "Depth", D)
statistics: T = cluster (Z, "Cutoff", C, "Criterion", criterion)
statistics: T = cluster (Z, "MaxClust", N)

Define clusters from an agglomerative hierarchical cluster tree.

Given a hierarchical cluster tree Z generated by the `linkage` function, `cluster` defines clusters, using a threshold value C to identify new clusters (’Cutoff’) or according to a maximum number of desired clusters N (’MaxClust’).

criterion is used to choose the criterion for defining clusters, which can be either "inconsistent" (default) or "distance". When using "inconsistent", `cluster` compares the threshold value C to the inconsistency coefficient of each link; when using "distance", `cluster` compares the threshold value C to the height of each link. D is the depth used to evaluate the inconsistency coefficient, its default value is 2.

`cluster` uses "distance" as a criterion for defining new clusters when it is used the ’MaxClust’ method.

See also: clusterdata, dendrogram, inconsistent, kmeans, linkage, pdist

Source Code: cluster