[scikit-learn] hierarchical clustering

[Jaime Lopez Carvajal]

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>
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>
> 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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