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.
||(L, M) = leaders(Z, T):
These are routines for agglomerative clustering.
|linkage(y[, method, metric])
||Performs hierarchical/agglomerative clustering on the condensed distance matrix y.
||Performs single/min/nearest linkage on the condensed distance matrix y.
||Performs complete complete/max/farthest point linkage on the condensed distance matrix y.
||Performs average/UPGMA linkage on the condensed distance matrix
||Performs weighted/WPGMA linkage on the condensed distance matrix
||Performs centroid/UPGMC linkage. See linkage for more
||Performs median/WPGMC linkage. See linkage for more
||Performs Ward’s linkage on a condensed or redundant distance
These routines compute statistics on hierarchies.
||Calculates the cophenetic distances between each observation in
||Converts a linkage matrix generated by MATLAB(TM) to a new
||Calculates inconsistency statistics on a linkage.
||Returns the maximum inconsistency coefficient for each non-singleton cluster and its descendents.
||Returns the maximum distance between any cluster for each non-singleton cluster.
|maxRstat(Z, R, i)
||Returns the maximum statistic for each non-singleton cluster and its descendents.
||Converts a linkage matrix Z generated by the linkage function
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
|ClusterNode(id[, left, right, dist, count])
||A tree node class for representing a cluster.
||Returns a list of leaf node ids (corresponding to observation vector index) as they appear in the tree from left to right.
||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.
||Determines if two different cluster assignments T1 and
||Returns True if the linkage passed is monotonic. The linkage
||Checks if a linkage matrix Z and condensed distance matrix
||Returns the number of original observations of the linkage matrix passed.
Utility routines for plotting:
||Changes the list of matplotlib color codes to use when coloring links with the dendrogram color_threshold feature.
|[Gow69]||Gower, JC and Ross, GJS. “Minimum Spanning Trees and Single Linkage
Cluster Analysis.” Applied Statistics. 18(1): pp. 54–64. 1969.|
|[War63]||Ward Jr, JH. “Hierarchical grouping to optimize an objective
function.” Journal of the American Statistical Association. 58(301):
pp. 236–44. 1963.|
|[Joh66]||Johnson, SC. “Hierarchical clustering schemes.” Psychometrika.
32(2): pp. 241–54. 1966.|
|[Sne62]||Sneath, PH and Sokal, RR. “Numerical taxonomy.” Nature. 193: pp.
|[Bat95]||Batagelj, V. “Comparing resemblance measures.” Journal of
Classification. 12: pp. 73–90. 1995.|
|[Sok58]||Sokal, RR and Michener, CD. “A statistical method for evaluating
systematic relationships.” Scientific Bulletins. 38(22):
pp. 1409–38. 1958.|
|[Ede79]||Edelbrock, C. “Mixture model tests of hierarchical clustering
algorithms: the problem of classifying everybody.” Multivariate
Behavioral Research. 14: pp. 367–84. 1979.|
|[Jai88]||Jain, A., and Dubes, R., “Algorithms for Clustering Data.”
Prentice-Hall. Englewood Cliffs, NJ. 1988.|
|[Fis36]||Fisher, RA “The use of multiple measurements in taxonomic
problems.” Annals of Eugenics, 7(2): 179-188. 1936|
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Copyright (C) Damian Eads, 2007-2008. New BSD License.