Are there techiques for determining/choosing the dimension of SOM?

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Fabrice Clerot

SOM and k-means share the same problem for the choice of the number of centers ; they are however very different algorithms (SOM is competitive and cooperative whereas k-means is competitive only)

my rule of thumb is simple : use as large a som as you can (say 25x25 when you do not even expect more than 50 meaningful clusters), then run a hierarchical aglomerative clustering on the neurons (irrespective of the populations captured by the neurons : you are here clustering neurons, not the training samples), inspect the dendogram and choose the number of clusters of neurons that fit your needs and gather the samples inside each cluster of neurons

when growing a very large som, i do not let the neighbourhood shrink to zero : i always keep a minimal neighbouhood radius of one ; it helps the map stay connected and smooth

Ozan Ozisik

SOM is not a version of k-means because SOM has the neighborhood aspect which lets it create a topological understanding of the clusters. Similar clusters are expected to be close to each other.

You may also consider Growing Self-Organizing Maps which add new nodes to the boundaries instead of creating a SOM with a lot of nodes.

Jesujoba Oluwadara Alabi

Thanks for all your responses.

Please Dr. Ozan Ozisik, can you assist me with where I can get the latest GSOM/GHSOM(toolbox) for MATLAB??

I will appreciate that sir.

Hi,

For MATLAB version I found this (There is also a version written in C++ on this page, and a link to newer Java version):

http://www.ifs.tuwien.ac.at/~andi/ghsom/download.html

http://www.ifs.tuwien.ac.at/~andi/ghsom/download/GHSOM_MATLAB.tar.gz

For Java:

http://www.ifs.tuwien.ac.at/dm/somtoolbox/index.html

For python:

https://github.com/philippludwig/pygsom

For R (it is removed from CRAN but formerly available versions can be obtained)

https://rdrr.io/cran/GrowingSOM/

Note that I did not use any of these.

Regards,

Ozan

Thanks Dr. Ozan. Grateful sir.