[scikit-learn] hierarchical clustering

[Jaime Lopez Carvajal]

Hi Roman,

I will check that function too.
Thanks for help.

Have a good day, Jaime

On Fri, Nov 4, 2016 at 4:28 AM, Roman Yurchak <rth.yurchak at gmail.com> wrote:

> Hi Jaime,
>
> Alternatively, in scikit learn I think, you could use
>    hac = AgglomerativeClustering(n_clusters, linkage="ward")
>    hac.fit(data)
>    clusters = hac.labels_
> there in an example on how to plot a dendrogram from this in
>    https://github.com/scikit-learn/scikit-learn/pull/3464
>
> AgglomerativeClustering internally calls scikit learn's version of
> cut_tree. I would be curious to know whether this is equivalent to
> scipy's fcluster.
>
> Roman
>
> On 03/11/16 23:12, Jaime Lopez Carvajal wrote:
> > Hi Juan,
> >
> > The fcluster function was that I needed. I can now proceed from here to
> > classify images.
> > Thank you very much,
> >
> > Jaime
> >
> > On Thu, Nov 3, 2016 at 5:00 PM, Juan Nunez-Iglesias <jni.soma at gmail.com
> > <mailto:jni.soma at gmail.com>> wrote:
> >
> >     Hi Jaime,
> >
> >     From /Elegant SciPy/:
> >
> >     """
> >     The *fcluster* function takes a linkage matrix, as returned by
> >     linkage, and a threshold, and returns cluster identities. It's
> >     difficult to know a-priori what the threshold should be, but we can
> >     obtain the appropriate threshold for a fixed number of clusters by
> >     checking the distances in the linkage matrix.
> >
> >     from scipy.cluster.hierarchy import fcluster
> >     n_clusters = 3
> >     threshold_distance = (Z[-n_clusters, 2] + Z[-n_clusters+1, 2]) / 2
> >     clusters = fcluster(Z, threshold_distance, 'distance')
> >
> >     """
> >
> >     As an aside, I imagine this question is better placed in the SciPy
> >     mailing list than scikit-learn (which has its own hierarchical
> >     clustering API).
> >
> >     Juan.
> >
> >     On Fri, Nov 4, 2016 at 2:16 AM, Jaime Lopez Carvajal
> >     <jalopcar at gmail.com <mailto:jalopcar at gmail.com>> wrote:
> >
> >         Hi there,
> >
> >         I am trying to do image classification using hierarchical
> >         clustering.
> >         So, I have my data, and apply this steps:
> >
> >         from scipy.cluster.hierarchy import dendrogram, linkage
> >
> >         data1 = np.array(data)
> >         Z = linkage(data, 'ward')
> >         dendrogram(Z, truncate_mode='lastp',  p=12,
> >         show_leaf_counts=False, leaf_rotation=90.,
> >         leaf_font_size=12.,show_contracted=True)
> >         plt.show()
> >
> >         So, I can see the dendrogram with 12 clusters as I want, but I
> >         dont know how to use this to classify the image.
> >         Also, I understand that funtion cluster.hierarchy.cut_tree(Z,
> >         n_clusters), that cut the tree at that number of clusters, but
> >         again I dont know how to procedd from there. I would like to
> >         have something like: cluster = predict(Z, instance)
> >
> >         Any advice or direction would be really appreciate,
> >
> >         Thanks in advance, Jaime
> >
> >
> >         --
> >         /*Jaime Lopez Carvajal
> >         */
> >
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> >
> >
> >
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> >
> >
> >
> >
> > --
> > /*Jaime Lopez Carvajal
> > */
> >
> >
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-- 

*Jaime Lopez Carvajal*
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