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I'm trying to cluster set of histograms. The histograms represent the frequencies of the distribution for a numbers from 1 to 5. The following figure shows two samples of my data.

enter image description here

I have 10,000 histograms with fixed number of bins (5) and I'm looking for a simple clustering algorithm implemented in MATLAB, C# or C++, that can take the histograms and cluster them.

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    $\begingroup$ Take a look here and here. I couldn't find an ungated copy of the second article. $\endgroup$ – shadowtalker May 15 '15 at 16:20
  • $\begingroup$ Unfortunately, the second article is worth a mint. :) Thanks for this links! $\endgroup$ – Michael Dorner Jul 25 '15 at 10:02
  • $\begingroup$ I might try to use PCA to group them. It is 5-dimensional continuous data, and you are trying to pack it into discrete bins. $\endgroup$ – EngrStudent Dec 23 '16 at 23:27
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Use hierarchical clustering or DBSCAN.

They have one huge benefit over k-means: they work with arbitrary distance measures, and with histograms you might want to use like, for example, Jensen-Shannon divergence, etc. that are designed to capture the similarity of distributions.

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K-means could do this. K-means is an unsupervised clustering algorithm. Rewrite each histogram as a vector and use Euclidean distance.

This post goes into the assumptions of K-means: How to understand the drawbacks of K-means You might want to check these.

You have to determine the number of clusters yourself by estimating models with different k.

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