Hi Roman,

I reduced my original data set with feature selection, it has now n_samples=10467, n_features=23.

I tried clustering with Birch algorithm this time it worked.

I obtained 35 clusters for the reduced dataset in the attachment(data2.dat).

How can i know which cluster member represents best each cluster?

For example Cluster 0 has 5 member which are : 1, 2, 3, 28 and 29. rows in the data set.

Which cluster member (1, 2, 3, 28 or 29) represents best Cluster 0 ?

In the birch code i use this code line: centroids = brc.subcluster_centers_

How do I interpret this line of code output?

Thank you so much for your help.

Birch Code:

from sklearn.cluster import Birch

from io import StringIO

import numpy as np

X=np.loadtxt(open("C:\data2.dat", "rb"), delimiter=",")

brc = Birch(branching_factor=50, n_clusters=None, threshold=0.5,compute_labels=True,copy=True)

brc.fit(X)

centroids = brc.subcluster_centers_

labels = brc.subcluster_labels_

brc.predict(X)

print("\n brc.predict(X)")

print(brc.predict(X))

print("\n centroids")

print(centroids)

print("\n labels")

print(labels)

On Mon, Jul 3, 2017 at 11:46 PM, Roman Yurchak <rth.yurchak@gmail.com> wrote:

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.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
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