Hi Andy & Ken,
Thanks Ken for the alternative but I am using a cosine distance.
Andy, concerning the computation of the mean, the function has to be
configurable too but the default function mean is also good for cosine &
bregman divergence (http://www-users.cs.umn.edu/~kumar/dmbook/ch8.pdf see
table 8.2 page 501). Yes I could implement easily k-means but I will lose lot
of benefits from sklearn frameworks such as the ability to compare easily
several unsupervised algorithms. I was simply expected the distance function
to be configurable as it is with many other sklearn functions.
On the other hand, do you know why
metrics.cluster.unsupervised.silhouette_score required the labels? I understand
that we can compute the supervised version of the silhouette score but was
looking for the unsupervised version. Even the help doesn’t mention anywhere
the labels.
I am trying to push for sklearn in my team, quite impress so far.
Thanks,
Francis
From: Kenneth C. Arnold [mailto:[email protected]]
Sent: April-02-13 3:32 PM
To: [email protected]
Subject: Re: [Scikit-learn-general] kmeans distance function not configurable
If you want a Mahalanobis distance, though, you can instead just transform your
data using the Cholesky decomposition of the distance matrix.
-Ken
On Tue, Apr 2, 2013 at 3:09 PM, Andreas Mueller
<[email protected]<mailto:[email protected]>> wrote:
Hi Francis.
No. It is highly non-trivial for most distance functions to do k-means as
the computation of the mean has to be replaced by a different computation.
If you know how to do that, implementing k-means in pure numpy is not all that
hard.
This question comes up quite a lot. Maybe we should do a faq or something.
Cheers,
Andy
On 04/02/2013 09:05 PM, Pieraut, Francis wrote:
Hi guys,
Is there is simple way to change the distance function used in the kmeans
implementation?
Thanks,
Francis
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