Hi,
I need to cluster some integer data where the features are an unordered
set, that is the two features
[20, 1, 10] and
[ 1, 20, 10] are equivalent and should be in the same cluster.
I think this is essentially similar to association rule data mining. Does
anyone know how this can be achieved using sklearn? If not, can someone
recommend me a suitable python package for clustering this type of data?
The data refer to image pixels locations, so each feature will be an
integer ranging from 0 to potentially a few million. I'm likely to cluster
problems of size 100,000 samples by 200 features (i.e. 200 pixel locations
in each set).
Thanks,
Martin
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