[scikit-learn] How to get centroids from SciPy's hierarchical agglomerative clustering?

[Andreas Mueller]

The centroids don't "represent" the clusters, though, and you can 
construct arbitrary complex
clusterings where all the centroids are identical.

On 10/20/2017 01:08 PM, Sebastian Raschka wrote:
> Independent from the implementation, and unless you use the 'centroid' or 'average linkage' method, cluster centroids don't need to be computed when performing the agglomerative hierarchical clustering . But you can always compute it manually by simply averaging all samples from a cluster (for each feature).
>
> Best.
> Sebastian
>
>> On Oct 20, 2017, at 9:13 AM, Sema Atasever <s.atasever at gmail.com> wrote:
>>
>> Dear scikit-learn members,
>>
>> I am using SciPy's hierarchical agglomerative clustering methods to cluster a
>> 1000 x 22 matrix of features, after clustering my data set with scipy.cluster.hierarchy.linkage and and assigning each sample to a cluster,
>> I can't seem to figure out how to get the centroid from the resulting clusters.
>> I would like to extract one element or a few out of each cluster, which is the closest to that cluster's centroid.
>>
>> Below follows my code:
>>
>> D=np.loadtxt(open("C:\dataset.txt", "rb"), delimiter=";")
>> Y = hierarchy.linkage(D, 'ward')
>> assignments = hierarchy.fcluster(Y, 5, criterion="maxclust")
>>
>> I am taking my matrix of features, computing the euclidean distance between them, and then passing them onto the hierarchical clustering method. From there, I am creating flat clusters, with a maximum of 5 clusters
>>
>> Now, based on the flat clusters assignments, how do I get the 1 x 22 centroid that represents each flat cluster?
>>
>> Best.
>> <SciPy_python_codes.py><dataset.txt><assignments.out>_______________________________________________
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