`scipy.cluster.hierarchy._hierarchy_impl:fclusterdata`

source: build-install/usr/lib/python3.14/site-packages/scipy/cluster/hierarchy/_hierarchy_impl.py:2610

Signature

def fclusterdata(X, t, criterion = inconsistent, metric = euclidean, depth = 2, method = single, R = None)

Summary

Cluster observation data using a given metric.

Extended Summary

Clusters the original observations in the n-by-m data matrix X (n observations in m dimensions), using the euclidean distance metric to calculate distances between original observations, performs hierarchical clustering using the single linkage algorithm, and forms flat clusters using the inconsistency method with t as the cut-off threshold.

A 1-D array T of length n is returned. T[i] is the index of the flat cluster to which the original observation i belongs.

Parameters

X : (N, M) ndarray

N by M data matrix with N observations in M dimensions.

t : scalar

For criteria 'inconsistent', 'distance' or 'monocrit',: this is the threshold to apply when forming flat clusters.
For 'maxclust' or 'maxclust_monocrit' criteria,: this would be max number of clusters requested.

criterion : str, optional

Specifies the criterion for forming flat clusters. Valid values are 'inconsistent' (default), 'distance', or 'maxclust' cluster formation algorithms. See fcluster for descriptions.

metric : str or function, optional

The distance metric for calculating pairwise distances. See distance.pdist for descriptions and linkage to verify compatibility with the linkage method.

depth : int, optional

The maximum depth for the inconsistency calculation. See inconsistent for more information.

method : str, optional

The linkage method to use (single, complete, average, weighted, median centroid, ward). See linkage for more information. Default is "single".

R : ndarray, optional

The inconsistency matrix. It will be computed if necessary if it is not passed.

Returns

fclusterdata : ndarray: A vector of length n. T[i] is the flat cluster number to which original observation i belongs.

Notes

This function is similar to the MATLAB function clusterdata.

Examples

from scipy.cluster.hierarchy import fclusterdata

✓

This is a convenience method that abstracts all the steps to perform in a typical SciPy's hierarchical clustering workflow. * Transform the input data into a condensed matrix with `scipy.spatial.distance.pdist`. * Apply a clustering method. * Obtain flat clusters at a user defined distance threshold ``t`` using `scipy.cluster.hierarchy.fcluster`.

X = [[0, 0], [0, 1], [1, 0],
     [0, 4], [0, 3], [1, 4],
     [4, 0], [3, 0], [4, 1],
     [4, 4], [3, 4], [4, 3]]

✓

fclusterdata(X, t=1)

✓

array([3, 3, 3, 4, 4, 4, 2, 2, 2, 1, 1, 1], dtype=int32)

The output here (for the dataset ``X``, distance threshold ``t``, and the default settings) is four clusters with three data points each.

Aliases

scipy.cluster.hierarchy.fclusterdata