Hierarchical clustering (scipy.cluster.hierarchy) — SciPy v1.15.2 Manual (original) (raw)
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:
Utility classes:
| DisjointSet([elements]) | Disjoint set data structure for incremental connectivity queries. |
|---|