Scikit-learn: Examples using precomputed distance matrix for clustering
Hi, I want to use clustering methods with precomputed distance matrix (NxN). I found DBSCAN has "metric" attribute but can't find examples to follow. Can you please help. Examples for other clustering methods are also very helpful. Thanks.
celiafish
All 4 comments
what part are you unclear about?
use metric="precomputed" and provide a as X a precomputed similarity matrix.
See http://scikit-learn.org/dev/auto_examples/manifold/plot_mds.html for an example using MDS.
amueller
on 11 Dec 2015
no, it's not feasible algorithmically for k means
jnothman
on 9 Feb 2018
Can we change the type of distance to cosine similarity? Thanks!
isukrit
on 9 Feb 2018
no. We have a PR in the works for K medoid which is a related algorithm
that can take an arbitrary distance metric. Try it out: #7694. K means
needs to repeatedly calculate Euclidean distance from each point to an
arbitrary vector, and requires the mean to be meaningful; it cannot work
with a metric of your choice.
jnothman
on 9 Feb 2018
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Most helpful comment
what part are you unclear about?
use metric="precomputed" and provide a as X a precomputed similarity matrix.
See http://scikit-learn.org/dev/auto_examples/manifold/plot_mds.html for an example using MDS.