This feels a bit like it should be a meta-estimator using an arbitrary
clustering algorithm to create a divisive one.
That would easily allow the PCA thing.
On 05/17/2015 07:44 PM, Joel Nothman wrote:
Hi Sam,
I think this could be interesting. You could allow for learning
parameters on each sub-cluster by accepting a transformer as a
parameter, then using sample =
sklearn.base.clone(transformer).fit_transform(sample).
I suspect bisecting k-means is notable enough and different enough for
inclusion. Seeing as you have an implementation, I would suggest
constructing a small example (preferably on real-world data) that
highlights the superiority or distinctiveness of this approach. Once
you have something illustrative, submit a PR with the output and see
how people review it.
In terms of establishing a fixed algorithm, is the criterion for which
cluster is next expanded standard in the literature? Are there
alternatives? (I've not read up yet.)
Thanks,
Joel
On 17 May 2015 at 06:43, Sam Schetterer <samsc...@gmail.com
<mailto:samsc...@gmail.com>> wrote:
Andreas,
There isn't necessarily a linkage function defined, at least in
the sense of agglomerative clustering, since this is not comparing
clusters to merge but rather breaking them up. The clusters are
split using another clustering algorithm supplied by the caller.
The most common one that I've found in literature is kmeans with 2
clusters, which leads to a binary tree structure and is generally
referred to as bisecting kmeans (used for example in the first
citation). One could use any clustering algorithm, even have two
different ones that are used in different conditions (spectral
clustering when n < 1000 and kmeans otherwise, for example).
In addition, with divisive clustering, one can refine the distance
metric for various tree branches which I don't think is possible
with hierarchical clustering. I've done this with text clustering
to get more accurate tf-idf deeper in the hierarchy and the second
paper I cited in the original email performs the SVD at each new
level.
You bring up a good point about divisive vs agglomerative being an
implementation detail although I think for certain uses, it may be
very important. If it's expensive to compute a connectivity
matrix, a bisecting kmeans will perform significantly better than
the agglomerative methods on larger datasets.
Best,
Sam
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