The Self-Organising Map is a popular unsupervised neural network model which has successfully been used for clustering
various kinds of data.
The SOMVIS Package is an add-on for the Matlab SOMToolbox. It provides a graphical interface to access a set of visualisations, SOM quality measures, as well as clustering techniques such as k-means and Ward's linkage that can be applied on the SOM lattice.
Contact: Andreas Rauber.
- Metro Map
- Gradient Field & Borderline
- Neighbourhood Graphs (Graphical Methods)
- D-Matrix (variation of U-Matrix)
S. Kaski and K. Lagus. Comparing Self-Organizing Maps. In Proceedings of the International Conference on Artificial Neural Networks (ICANN '96), Bochum, Germany, July 16-19, pages 809-814, Berlin, 1996. Springer.
K. Kiviluoto.. Topology preservation in Self-Organizing Maps. In Proceedings of the IEEE International Conference on Artificial Neural Networks (ICANN'96), pages 294-299. Piscataway, New Jersey, USA, June 1996.
H. U. Bauer and K. R. Pawelzik. Quantifying the neighborhood preservation of Self-Organizing Feature Maps. In IEEE Transactions on Neural Networks, 3(4):570-579, July 1992.
Trustworthiness, Neighborhood Preservation
J. Venna and S. Kaski. Neighborhood preservation in nonlinear projection methods. An experimental study. In Proceedings of the Internationla Conference on Artificial Neural Networks (ICANN '01)pages 485-491. Berlin, 2001. Springer
Analytical ToolsWe additionally provide tools to further analyse the data and maps, we provide a set of additional methods:
To identify cluster boundaries, the SOM codebook (weight, model) vectors are clustered. SOMVIS provides the following methods:
- Linkage clustering, such as Single Linkage, Complete Linkage, and Ward's Linkage
- K-Means clustering
We provide visualisations of PCA, Sammons mapping and CCA projection methods.
Installation & How-To
Download the package
Note: The SOMVIS Package builds on these other packages:
- Matlab SOM Toolbox (http://www.cis.hut.fi/projects/somtoolbox/)
- SDH Toolbox (http://www.oefai.at/~elias/sdh/download.html)
- The "dijkstra.m" file (http://www.mathworks.nl/matlabcentral/fileexchange/loadFile.do?objectId=5550&objectType=file)
- Extract the package to a directory
- Start Matlab, and navigate to the directory
Run "setPaths" to set the (relative) paths to the needed libraries
Note: When setting the Matlab path to include all the previously mentioned packages & files manually, the "somvis" directory has to be included above the "somtoolbox" directory, since a function has been overwritten.
Train a map
Load a data set and train a SOM with it.
Creating a SOM from own data can be done with the SOM Toolbox commands
See the Matlab SOMToolbox manual for more details.
- Some pre-trained SOMs (along with their data sets) are included in the SOM-VIS package, and can be loaded with the Matlab command:
The following datasets are included in the
- Load a data set and train a SOM with it.
- Start the GUI with
somvis_gui (map, data)