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

Calculates 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 :

res : tuple

A tuple (c, {d}):

  • 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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