Thank you very much for your answer.
On Fri, Oct 20, 2017 at 10:07 PM, Andreas Mueller <t3k...@gmail.com> wrote:
> 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.atase...@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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