scipy.cluster.hierarchy.fclusterdata(X, t, criterion='inconsistent', metric='euclidean', depth=2, method='single', R=None)[source]#

Cluster observation data using a given metric.

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.

X(N, M) ndarray

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

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.

criterionstr, optional

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

metricstr or function, optional

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

depthint, optional

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

methodstr, optional

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

Rndarray, optional

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


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

See also


pairwise distance metrics


This function is similar to the MATLAB function clusterdata.


>>> 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.

>>> 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.