cut_tree — SciPy v1.15.2 Manual (original) (raw)

scipy.cluster.hierarchy.

scipy.cluster.hierarchy.cut_tree(Z, n_clusters=None, height=None)[source]#

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