Hello Sema,
as far as I can tell, in your dataset you has n_samples=65909,
n_features=539. Clustering high dimensional data is problematic for a
number of reasons,
https://en.wikipedia.org/wiki/Clustering_high-dimensional_data#Problems
besides the BIRCH implementation doesn't scale well for n_features >> 50
(see for instance the discussion in the second part of
https://github.com/scikit-learn/scikit-learn/pull/8808#issuecomment-300776216
also in ).
As a workaround for the memory error, you could try using the
out-of-core version of Birch (using `partial_fit` on chunks of the
dataset, instead of `fit`) but in any case it might also be better to
reduce dimensionality beforehand (e.g. with PCA), if that's acceptable.
Also the threshold parameter may need to be increased: since in your
dataset it looks like the Euclidean distances are more in the 1-10 range?
--
Roman
On 03/07/17 17:09, Sema Atasever wrote:
Dear Roman,
When I try the code with the original data (*data.dat*) as you
suggested, I get the following error : *Memory Error* --> (*error.png*),
how can i overcome this problem, thank you so much in advance.
data.dat
<https://drive.google.com/file/d/0B4rY6f4kvHeCYlpZOURKNnR0Q1k/view?usp=drive_web>
On Fri, Jun 30, 2017 at 5:42 PM, Roman Yurchak <rth.yurc...@gmail.com
<mailto:rth.yurc...@gmail.com>> wrote:
Hello Sema,
On 30/06/17 17:14, Sema Atasever wrote:
I want to cluster them using Birch clustering algorithm.
Does this method have 'precomputed' option.
No it doesn't, see
http://scikit-learn.org/stable/modules/generated/sklearn.cluster.Birch.html
<http://scikit-learn.org/stable/modules/generated/sklearn.cluster.Birch.html>
so you would need to provide it with the original features matrix
(not the precomputed distance matrix). Since your dataset is fairly
small, there is no reason in precomputing it anyway.
I needed train an SVM on the centroids of the microclusters so
*How can i get the centroids of the microclusters?*
By "microclusters" do you mean sub-clusters? If you are interested
in the leaves subclusters see the Birch.subcluster_centers_ parameter.
Otherwise if you want all the centroids in the hierarchy of
subclusters, you can browse the hierarchical tree via the
Birch.root_ attribute then look at _CFSubcluster.centroid_ for each
subcluster.
Hope this helps,
--
Roman
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