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])	Form flat clusters from the hierarchical clustering defined by the given linkage matrix.
fclusterdata(X, t[, criterion, metric, …])	Cluster observation data using a given metric.
leaders(Z, T)	Return the root nodes in a hierarchical clustering.

These are routines for agglomerative clustering.

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

These routines compute statistics on hierarchies.

cophenet(Z[, Y])	Calculate the cophenetic distances between each observation in the hierarchical clustering defined by the linkage Z.
from_mlab_linkage(Z)	Convert a linkage matrix generated by MATLAB(TM) to a new linkage matrix compatible with this module.
inconsistent(Z[, d])	Calculate inconsistency statistics on a linkage matrix.
maxinconsts(Z, R)	Return the maximum inconsistency coefficient for each non-singleton cluster and its children.
maxdists(Z)	Return the maximum distance between any non-singleton cluster.
maxRstat(Z, R, i)	Return the maximum statistic for each non-singleton cluster and its children.
to_mlab_linkage(Z)	Convert a linkage matrix to a MATLAB(TM) compatible one.

Routines for visualizing flat clusters.

dendrogram(Z[, p, truncate_mode, …])

Plot 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)	Return a list of leaf node ids.
to_tree(Z[, rd])	Convert a linkage matrix into an easy-to-use tree object.
cut_tree(Z[, n_clusters, height])	Given a linkage matrix Z, return the cut tree.
optimal_leaf_ordering(Z, y[, metric])	Given a linkage matrix Z and distance, reorder the cut tree.

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])	Return True if the inconsistency matrix passed is valid.
is_valid_linkage(Z[, warning, throw, name])	Check the validity of a linkage matrix.
is_isomorphic(T1, T2)	Determine if two different cluster assignments are equivalent.
is_monotonic(Z)	Return True if the linkage passed is monotonic.
correspond(Z, Y)	Check for correspondence between linkage and condensed distance matrices.
num_obs_linkage(Z)	Return 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 use by dendrogram.

Utility classes:

DisjointSet([elements])

Disjoint set data structure for incremental connectivity queries.

scipy.cluster.vq.kmeans2 scipy.cluster.hierarchy.fcluster