The end goal is to place the images into several buckets (clusters) according to the calculated similarity measures - CW-SSIM.
The task seems to be trivial, but I can't figure out the best way to handle "similarity based" clustering in scikit-learn. K-Means clustering looks like a good choice, but it doesn't accept any "comparison functions" or custom distance functions.
So how to handle the comparison based (similarity based) clustering in scikit-learn?
I was thinking about "comparison matrices" with 1 (similar) or 0 (not similar) per cell according to the calculated CW-SSIM similarity values. This matrix will be used for fitting into K-Means clustering. But then we will face the scalability issue, because such matrix will have dimensions equal to the amount of images ... which might grow to 1+ million in the future.
If there is an easier option in R than in Python, then I'm ready to review as well.
Thanks in advance.
UPDATE from Jan 18, 2016
I've created some code on GitHub about this topic: https://github.com/llvll/imgcluster
This project also includes IP[y] Notebook with step-by-step instructions and extra comments: https://github.com/llvll/imgcluster/blob/master/ip%5By%5D/imgcluster.ipynb