**SOM Toolbox** is a software library for Matlab 5 (version 5.2
at least) implementing the Self-Organizing Map (SOM) algorithm.

** SOM_PAK** is a public domain software
package, written in C language for UNIX and PC environments. It is a
semi-official implementation of the SOM algorithm.

**Matlab**
has been steadily gaining popularity as the "language of scientific
computing". For quite a while, the Matlab Neural Networks Toolbox has
included a couple of functions that are related to the SOM. These are,
however, primarily for demonstrations of the self-organization process
and, as such, not sufficient for practical data analysis
applications. As far as we know the SOM Toolbox is the first such
software for Matlab.

So there seemed to be a need also for a Matlab implementation of
the algorithms found in `SOM_PAK`. Moreover, we felt that
Matlab might be well-suited for fast prototyping and customizing as Matlab employs a
high-level programming language with strong support for graphics and
visualization. It is because of these two reasons that we considered
it worthwhile to develop the SOM Toolbox.

The `SOM_PAK` files can
also be accessed with the Toolbox, so it is possible to first train
the map with the `SOM_PAK` and then use the Toolbox for map
visualization. Read more about SOM Toolbox vs. SOM_PAK >>>.

**Highlights** of the SOM Toolbox include the following:

- Modular programming style: the Toolbox code utilizes Matlab structures and the functions are constructed in a modular manner, which makes it convenient to tailor the code for each users' specific needs
- Component names, masks and normalizations: to facilate data mining process, the input vector components may be given names, and different kinds of (reversible) preprocessing operations can be defined for them. Also, the components may be masked, or weighted, according to their relative importance
- Batch or sequential training: in data analysis applications, the speed of training may be considerably improved by using the batch version. There are also other training variants, like supervised SOM.
- Map dimension: maps may be N-dimensional --- although visualization is not supported when N > 2
- Advanced graphics: building on the Matlab's strong graphics capabilities, attractive figures can be easily produced
- GUIs: there are also some graphical user interfaces, although the use of command line versions of the functions is strongly recommended

You are at: CIS → SOM Toolbox: introduction

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