The SelfOrganising Map is a popular unsupervised neural network model which has successfully been used for clustering
various kinds of data.
The SOMVIS Package is an addon 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 kmeans and Ward's linkage that can be applied on the SOM lattice.
Contact: Andreas Rauber.
Features
Visualisation
Besides the UMatrix and Component Plane visualisations, which are already included in the Matlab SOMToolbox, the
SOMVIS package additionally provides the following visualisations:
 Metro Map
 Gradient Field & Borderline
 Neighbourhood Graphs (Graphical Methods)
 PMatrix
 U*Matrix
 DMatrix (variation of UMatrix)
Quality Measures
The SOMVIS package additionally provides the following visualisations:

Intrinsic Distance
S. Kaski and K. Lagus. Comparing SelfOrganizing Maps. In Proceedings of the International Conference on Artificial Neural Networks (ICANN '96), Bochum, Germany, July 1619, pages 809814, Berlin, 1996. Springer. 
Topographic Error
K. Kiviluoto.. Topology preservation in SelfOrganizing Maps. In Proceedings of the IEEE International Conference on Artificial Neural Networks (ICANN'96), pages 294299. Piscataway, New Jersey, USA, June 1996. 
Topographic Product
H. U. Bauer and K. R. Pawelzik. Quantifying the neighborhood preservation of SelfOrganizing Feature Maps. In IEEE Transactions on Neural Networks, 3(4):570579, 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 485491. Berlin, 2001. Springer
Analytical Tools
We additionally provide tools to further analyse the data and maps, we provide a set of additional methods:
Map Clustering
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
 KMeans clustering

Data Projections
We provide visualisations of PCA, Sammons mapping and CCA projection methods.
License
The SOMVIS Matlab Visualisation Package for SelfOrganising Maps is licensed under the GPL License, Version 3.0.
This means you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation;
either version 3 of the License, or (at your option) any later version.
Installation & HowTo

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 commandssom_data_struct
,som_normalize
andsom_make
.
See the Matlab SOMToolbox manual for more details.  Some pretrained SOMs (along with their data sets) are included in the SOMVIS package, and can be loaded with the Matlab command:
load datasetName
The following datasets are included in thedata/
directory: Boston.mat
 Cars93.mat
 chickwts.mat
 epil.mat
 frac_big.mat
 frac.mat
 gilgais.mat
 ionosphere_big.mat
 ionosphere.mat
 iris_big.mat
 iris.mat
 mtcars.mat
 nlschools.mat
 phonetic_big.mat
 phonetic.mat
 phonetic_reduced.mat
 pluton.mat
 quakes.mat
 rock.mat
 UScereal.mat
 UScrime.mat
 xclara.mat

Load a data set and train a SOM with it.
 Start the GUI with
somvis_gui (map, data)