# How do i compare two Self Organizing Maps?

I have results (weights) for multiple runs of self organizing map. I am trying to compare these results to check if my algorithm gets to the same solution from different random initial weights. I have looked online but couldn't find any good method for this, any suggestions or helpful link will be appreciated.

Effort made; Compute distances of all SOMS against each other in MATLAB..(got stuck with a big matrix of distances), this doesn't seem right to me, i believe there should be a formal and better way out there.

This can also be applied to K-means. Weights are same as Centroids.

• I'm not familiar with SOMs, but since you bring up k-means, a common approach is to train them until weights to see if they're within a particular tolerance. Does this fit your interest? If so, I can write up a full answer. Dec 10 '15 at 19:12
• Thanks @SeanEaster yes let's pretend it is k-means as the results are all the same; (centroids). I have done the training bit, now i'm not sure on how to go about examining if they're within a particular tolerance kindly advice. Dec 14 '15 at 21:46
• If you find the same centroids I guess all of them found the same local minimum Dec 14 '15 at 23:18

First of all, if you want to use R language, there is a very useful package (actually not only one) to do your analyses: "kohonen". The most simple function for unsupervised Self Organizing Maps (I think it's your case) is "som". Now suppose your trained som is called mySom.
index <- mean(mySom$distances)  In order to see if they produce similar results you should see the how the codebooks (vectors) are similar from a SOM to another (mySom$codes). I think you could make the average difference between codebooks.
Using the parameter type inside the plot function you can represent the SOM is many ways. The most useful are: type = "mapping", "property".