SciPy

scipy.cluster.hierarchy.cophenet

scipy.cluster.hierarchy.cophenet(Z, Y=None)[source]

Calculate the cophenetic distances between each observation in the hierarchical clustering defined by the linkage Z.

Suppose p and q are original observations in disjoint clusters s and t, respectively and s and t are joined by a direct parent cluster u. The cophenetic distance between observations i and j is simply the distance between clusters s and t.

Parameters:
Z : ndarray

The hierarchical clustering encoded as an array (see linkage function).

Y : ndarray (optional)

Calculates the cophenetic correlation coefficient c of a hierarchical clustering defined by the linkage matrix Z of a set of \(n\) observations in \(m\) dimensions. Y is the condensed distance matrix from which Z was generated.

Returns:
c : ndarray

The cophentic correlation distance (if Y is passed).

d : ndarray

The cophenetic distance matrix in condensed form. The \(ij\) th entry is the cophenetic distance between original observations \(i\) and \(j\).

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