weighted — SciPy v1.15.3 Manual (original) (raw)

scipy.cluster.hierarchy.

scipy.cluster.hierarchy.weighted(y)[source]#

Perform weighted/WPGMA linkage on the condensed distance matrix.

See linkage for more information on the return structure and algorithm.

Parameters:

yndarray

The upper triangular of the distance matrix. The result ofpdist is returned in this form.

Returns:

Zndarray

A linkage matrix containing the hierarchical clustering. Seelinkage for more information on its structure.

Examples

from scipy.cluster.hierarchy import weighted, fcluster from scipy.spatial.distance import pdist

First, we need a toy dataset to play with:

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:

Finally, we can perform the clustering:

Z = weighted(y) Z array([[ 0. , 1. , 1. , 2. ], [ 6. , 7. , 1. , 2. ], [ 3. , 4. , 1. , 2. ], [ 9. , 11. , 1. , 2. ], [ 2. , 12. , 1.20710678, 3. ], [ 8. , 13. , 1.20710678, 3. ], [ 5. , 14. , 1.20710678, 3. ], [10. , 15. , 1.20710678, 3. ], [18. , 19. , 3.05595762, 6. ], [16. , 17. , 3.32379407, 6. ], [20. , 21. , 4.06357713, 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, 1, 2, 3, 10, 11, 12, 4, 6, 5], dtype=int32) fcluster(Z, 1.5, criterion='distance') array([3, 3, 3, 1, 1, 1, 4, 4, 4, 2, 2, 2], dtype=int32) fcluster(Z, 4, criterion='distance') array([2, 2, 2, 1, 1, 1, 2, 2, 2, 1, 1, 1], dtype=int32) fcluster(Z, 6, 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.