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I do have about 700 matrices (200*200 pixels) and I would like to cluster them into 5-10 groups.

I wanted to try the k-medoids method for this but am not sure how to implement it. According to what I found out I would have to convert the 200*200 matrix into a 40000 element vector and then use that as input to the clustering. However I am not very convinced about the ability of this algorithm to handle such high dimensional data.

I read about convolutional neural networks and it seemed more suitable. However I would like to know if there is maybe a simpler algorithm that could do the job instead.

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  • $\begingroup$ This is certainly broad, but given the upvoted & accepted answer, I don't think it is too broad to be answerable. I'm voting to leave open. $\endgroup$ – gung - Reinstate Monica Apr 6 '17 at 20:54
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Images clustering using the pixels as features is very unlikely to work well. Standard practice is to embed the images in a feature space designed for images, then apply a clustering method on that feature space. Standard feature representations include HOG (Histogram of Oriented gradients) and SIFT/SURF features. SIFT in particular is widely used for image search, which involves similar measurements of between image distances as for clustering.

You could use a convolutional neural network to embed the images in a lower dimensional space, then apply clustering in that space. It's a lot of work though. I wouldn't recommend it unless you already have experience with neural networks.

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    $\begingroup$ This answer let me to the right direction. I discovered that what I needed was to look at the similarity between my images (for example using the HOG method) and then do a clustering based on the distance only. $\endgroup$ – user3804227 Apr 6 '17 at 17:10
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Within Stanford's Course on Convolutional Neural Networks (CNN) you can find an implementation for k-Nearest Neighbor. I think the actual function for k-medoids can be applied to that.

The course will give you a good overview when to use CNN and why they are preferred over the nearest neighbor methods in general for larger datasets.

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