Hierarchical clustering (scipy.cluster.hierarchy)

These functions cut hierarchical clusterings into flat clusterings or find the roots of the forest formed by a cut by providing the flat cluster ids of each observation.

fcluster(Z, t[, criterion, depth, R, monocrit]) Forms flat clusters from the hierarchical clustering defined by
fclusterdata(X, t[, criterion, metric, ...]) Cluster observation data using a given metric.
leaders(Z, T) Returns the root nodes in a hierarchical clustering.

These are routines for agglomerative clustering.

linkage(y[, method, metric]) Performs hierarchical/agglomerative clustering on the condensed distance matrix y.
single(y) Performs single/min/nearest linkage on the condensed distance matrix y
complete(y) Performs complete/max/farthest point linkage on a condensed distance matrix
average(y) Performs average/UPGMA linkage on a condensed distance matrix
weighted(y) Performs weighted/WPGMA linkage on the condensed distance matrix.
centroid(y) Performs centroid/UPGMC linkage.
median(y) Performs median/WPGMC linkage.
ward(y) Performs Ward’s linkage on a condensed or redundant distance matrix.

These routines compute statistics on hierarchies.

cophenet(Z[, Y]) Calculates the cophenetic distances between each observation in
from_mlab_linkage(Z) Converts a linkage matrix generated by MATLAB(TM) to a new
inconsistent(Z[, d]) Calculates inconsistency statistics on a linkage.
maxinconsts(Z, R) Returns the maximum inconsistency coefficient for each non-singleton cluster and its descendents.
maxdists(Z) Returns the maximum distance between any non-singleton cluster.
maxRstat(Z, R, i) Returns the maximum statistic for each non-singleton cluster and its descendents.
to_mlab_linkage(Z) Converts a linkage matrix to a MATLAB(TM) compatible one.

Routines for visualizing flat clusters.

dendrogram(Z[, p, truncate_mode, ...]) Plots the hierarchical clustering as a dendrogram.

These are data structures and routines for representing hierarchies as tree objects.

ClusterNode(id[, left, right, dist, count]) A tree node class for representing a cluster.
leaves_list(Z) Returns a list of leaf node ids
to_tree(Z[, rd]) Converts a hierarchical clustering encoded in the matrix Z (by

These are predicates for checking the validity of linkage and inconsistency matrices as well as for checking isomorphism of two flat cluster assignments.

is_valid_im(R[, warning, throw, name]) Returns True if the inconsistency matrix passed is valid.
is_valid_linkage(Z[, warning, throw, name]) Checks the validity of a linkage matrix.
is_isomorphic(T1, T2) Determines if two different cluster assignments are equivalent.
is_monotonic(Z) Returns True if the linkage passed is monotonic.
correspond(Z, Y) Checks for correspondence between linkage and condensed distance matrices
num_obs_linkage(Z) Returns the number of original observations of the linkage matrix passed.

Utility routines for plotting:

set_link_color_palette(palette) Set list of matplotlib color codes for dendrogram color_threshold.


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[R2]“Hierarchical clustering.” API Reference Documentation. The Wolfram Research, Inc. http://reference.wolfram.com/mathematica/HierarchicalClustering/tutorial/ HierarchicalClustering.html. Accessed October 1, 2007.
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