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-30077621... 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_...>
On Fri, Jun 30, 2017 at 5:42 PM, Roman Yurchak <rth.yurchak@gmail.com <mailto:rth.yurchak@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 _______________________________________________ scikit-learn mailing list scikit-learn@python.org <mailto:scikit-learn@python.org> https://mail.python.org/mailman/listinfo/scikit-learn <https://mail.python.org/mailman/listinfo/scikit-learn>