[scikit-learn] Construct the microclusters using a CF-Tree

[Sema Atasever]

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 at 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#issue
> comment-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/0B4rY6f4kvHeCYlpZOURKNnR0Q1
>> k/view?usp=drive_web>
>> ​
>>
>> On Fri, Jun 30, 2017 at 5:42 PM, Roman Yurchak <rth.yurchak at gmail.com
>> <mailto:rth.yurchak at 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 at python.org <mailto:scikit-learn at python.org>
>>     https://mail.python.org/mailman/listinfo/scikit-learn
>>     <https://mail.python.org/mailman/listinfo/scikit-learn>
>>
>>
>>
>
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