[scikit-learn] Accessing Clustering Feature Tree in Birch
rth.yurchak at gmail.com
Wed Aug 23 07:28:16 EDT 2017
> what are the data samples in this cluster
Mehmet's response below works for exploring the hierarchical tree.
However, Birch currently doesn't store the data samples that belong to a
given subcluster. If you need that, as far as I know, a reasonable
approximation can be obtained by computing the data samples that are
closest to the centroid of the considered subcluster (accessible via
_CFNode.centroids_) as compared to all other subcluster centroids at
this hierarchical tree depth.
Alternatively, the modifications in PR
https://github.com/scikit-learn/scikit-learn/pull/8808 aimed to make
this process easier..
On 23/08/17 13:44, Suzen, Mehmet wrote:
> Hi Sema,
> You can access CFNode from the fit output, assign fit output, so you
> can have the object.
> brc_fit = brc.fit(X)
> brc_fit_cfnode = brc_fit.root_
> <sklearn.cluster.birch._CFNode object at 0x7ff31acbf668>
> Then you can access CFNode, see here
> Also, this example comparing mini batch kmeans.
> Hope this was what you are after.
> On 23 August 2017 at 10:55, Sema Atasever <s.atasever at gmail.com> wrote:
>> Dear scikit-learn members,
>> Considering the "CF-tree" data structure :
>> - How can i access Clustering Feature Tree in Birch?
>> - For example, how many clusters are there in the hierarchy under the root
>> node and what are the data samples in this cluster?
>> - Can I get them separately for 3 trees?
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>> scikit-learn at python.org
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