- scipy.cluster.hierarchy.cophenet(Z, Y=None)[source]¶
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.
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.
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 \(j\).