scipy.cluster.hierarchy.single#
- scipy.cluster.hierarchy.single(y)[source]#
Perform single/min/nearest linkage on the condensed distance matrix
y
.- Parameters
- yndarray
The upper triangular of the distance matrix. The result of
pdist
is returned in this form.
- Returns
- Zndarray
The linkage matrix.
See also
linkage
for advanced creation of hierarchical clusterings.
scipy.spatial.distance.pdist
pairwise distance metrics
Examples
>>> from scipy.cluster.hierarchy import single, fcluster >>> from scipy.spatial.distance import pdist
First, we need a toy dataset to play with:
x x x x x x x x x x x x
>>> X = [[0, 0], [0, 1], [1, 0], ... [0, 4], [0, 3], [1, 4], ... [4, 0], [3, 0], [4, 1], ... [4, 4], [3, 4], [4, 3]]
Then, we get a condensed distance matrix from this dataset:
>>> y = pdist(X)
Finally, we can perform the clustering:
>>> Z = single(y) >>> Z array([[ 0., 1., 1., 2.], [ 2., 12., 1., 3.], [ 3., 4., 1., 2.], [ 5., 14., 1., 3.], [ 6., 7., 1., 2.], [ 8., 16., 1., 3.], [ 9., 10., 1., 2.], [11., 18., 1., 3.], [13., 15., 2., 6.], [17., 20., 2., 9.], [19., 21., 2., 12.]])
The linkage matrix
Z
represents a dendrogram - seescipy.cluster.hierarchy.linkage
for a detailed explanation of its contents.We can use
scipy.cluster.hierarchy.fcluster
to see to which cluster each initial point would belong given a distance threshold:>>> fcluster(Z, 0.9, criterion='distance') array([ 7, 8, 9, 10, 11, 12, 4, 5, 6, 1, 2, 3], dtype=int32) >>> fcluster(Z, 1, criterion='distance') array([3, 3, 3, 4, 4, 4, 2, 2, 2, 1, 1, 1], dtype=int32) >>> fcluster(Z, 2, criterion='distance') array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int32)
Also,
scipy.cluster.hierarchy.dendrogram
can be used to generate a plot of the dendrogram.