scipy.cluster.hierarchy.cut_tree

scipy.cluster.hierarchy.cut_tree(Z, n_clusters=None, height=None)

Given a linkage matrix Z, return the cut tree.

Parameters

Zscipy.cluster.linkage array

The linkage matrix.

n_clustersarray_like, optional

Number of clusters in the tree at the cut point.

heightarray_like, optional

The height at which to cut the tree. Only possible for ultrametric trees.

Returns

cutreearray

An array indicating group membership at each agglomeration step. I.e., for a full cut tree, in the first column each data point is in its own cluster. At the next step, two nodes are merged. Finally, all singleton and non-singleton clusters are in one group. If n_clusters or height are given, the columns correspond to the columns of n_clusters or height.

Examples

>>> from scipy import cluster
>>> import numpy as np
>>> from numpy.random import default_rng
>>> rng = default_rng()
>>> X = rng.random((50, 4))
>>> Z = cluster.hierarchy.ward(X)
>>> cutree = cluster.hierarchy.cut_tree(Z, n_clusters=[5, 10])
>>> cutree[:10]
array([[0, 0],
       [1, 1],
       [2, 2],
       [3, 3],
       [3, 4],
       [2, 2],
       [0, 0],
       [1, 5],
       [3, 6],
       [4, 7]])  # random

scipy.cluster.hierarchy.to_tree scipy.cluster.hierarchy.optimal_leaf_ordering