scipy.cluster.hierarchy.optimal_leaf_ordering(Z, y, metric='euclidean')[source]

Given a linkage matrix Z and distance, reorder the cut tree.

Z : ndarray

The hierarchical clustering encoded as a linkage matrix. See linkage for more information on the return structure and algorithm.

y : ndarray

The condensed distance matrix from which Z was generated. Alternatively, a collection of m observation vectors in n dimensions may be passed as a m by n array.

metric : str or function, optional

The distance metric to use in the case that y is a collection of observation vectors; ignored otherwise. See the pdist function for a list of valid distance metrics. A custom distance function can also be used.

Z_ordered : ndarray

A copy of the linkage matrix Z, reordered to minimize the distance between adjacent leaves.


>>> from scipy.cluster import hierarchy
>>> np.random.seed(23)
>>> X = np.random.randn(10,10)
>>> Z = hierarchy.ward(X)
>>> hierarchy.leaves_list(Z)
array([0, 5, 3, 9, 6, 8, 1, 4, 2, 7], dtype=int32)
>>> hierarchy.leaves_list(hierarchy.optimal_leaf_ordering(Z, X))
array([3, 9, 0, 5, 8, 2, 7, 4, 1, 6], dtype=int32)