Hi,
I am trying to work out what, in precise mathematical terms,
[FeatureAgglomeration][1] does and would love some help. Here is some
example code:
import numpy as np
from sklearn.cluster import FeatureAgglomeration
for S in ['ward', 'average', 'complete']:
FA = FeatureAgglomeration(linkage=S)
print(FA.fit_transform(np.array([[-50,6,6,7,], [0,1,2,3]])))
This outputs:
[[ 6.33333333 -50. ]
[ 2. 0. ]]
[[ 6.33333333 -50. ]
[ 2. 0. ]]
[[ 6.33333333 -50. ]
[ 2. 0. ]]
Is it possible to say mathematically how these values have been computed?
Also, what exactly does linkage do and why doesn't it seem to make any
difference which option you choose?
Raphael
[1]:
http://scikit-learn.org/stable/modules/generated/sklearn.cluster.FeatureAgglomeration.html
PS I also asked at
https://stackoverflow.com/questions/51526616/what-does-featureagglomeration-compute-mathematically-and-when-does-linkage-make
_______________________________________________
scikit-learn mailing list
scikit-learn@python.org
https://mail.python.org/mailman/listinfo/scikit-learn
