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I am not sure if I can formulate this such that it is clear. :)

I have around 700 80x80 matrices, where each matrix shows some weather event (a matrix has continuous entries from 0 to 60). Now I would like to cluster these events into several event types.

I already used a very simple method where I transformed each matrix into a vector, and did kmeans on these vectors with euclidean distance.

But is there no better method, that uses somehow more information (e.g. neighboring pixels)? It seems to me, that I loose alot of information if I cluster in such a fashion.

So maybe it is a general question:

How to cluster matrices, where each entry has a "meaning" in 2 dimensions.

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  1. specify a distance or similarity measure that takes neighbor pixels into account. For example, a quadratic form may be helpful.

  2. compute a distance matrix, 700x700

  3. run hierarchical clustering

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  • $\begingroup$ Thank you for this suggestion. Do you know of good distance/ similarity measures for example radar images? $\endgroup$
    – thezebra
    Jun 5, 2015 at 12:42

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