Neural Connection 2.0

survey performed by Juha Vesanto, May 29th 1998

Neural Connection 2.0 is a neural networks tool for data analysis, prediction, classification, clustering and various other purposes. It is part of the SPSS program family. It has implementations of Multi-Layer Perceptron, Radial Basis Function, Bayesian Network, Kohonen Network, Closest Class Means Classifier, Regression, Principal Component Analysis, and various data handing tools.

The SOM (or Kohonen Network) is only one of the many tools in the program - and it suffers from it. Although the technical implementation of the SOM algorithm seems correct, the special features of the SOM have not been taken into account very well. Also the control of the training procedure is somewhat dubious. Major deficiencies include:

  1. map is always square in shape (except when it is 1-dimensional)
  2. although the neighborhood size can be made to decrease during training, this is not the default
  3. no map measures are implemented
  4. map visualization and post processing in general was really very poor
  5. training was a bit slow

In addition we had major stability problems with the program (in Windows NT 4.0). On the other hand, the GUI made training the maps easy and the presence of several other tools was a big bonus.

Disclaimer: we only experimented with the program for a short time. Therefore some deficiencies (especially regarding postprocessing) may be due to our lack of understanding the program. Still, in a good program such shouldn't happen.

Disclaimer: If any information on this page simply is not true, please tell us about it and we'll correct it ASAP.

Disclaimer: The opinions and observation herein should be considered personal of the person having performed by the survey, at the time of the survey. They do not reflect any official standing of his employer, of the Laboratory of Computer and Information Science or the Neural Networks Research Center.


Program name Neural Connection 2.0
Availability Commercial product, single license about $1500
Contact information on the web page:
Purpose Data analysis and decision support using neural networks
Operating system Windows
User interface GUI, script language
Good (in general)
Mediocre (regarding the SOM)
Documentation Online help
Good (user interface)
Mediocre (technical/scientific details)

SOM features

map parameters
Teaching algorithm standard SOM algorithm
- possibility of increasing map size during training by 'doubling' the nodes every N training steps
- implementation seems ok, but couldn't be properly verified
Map size 1- or 2-dimensional map grid
- initially 10x10 maximum size, but by using doubling this can be increased
- cannot have more map units than data vectors
Map lattice and shape rectangular lattice, sheet (rectangular) shape
Neighborhood function Function type: bubble (probably)
Neighborhood size:
Type: fixed / decreasing by a certain percentage each training cycle (possibly: d(t)=d0*(1-p/100)^t)
Parameters: initial neighborhood, decreasing rate
Learning rate: [type (linear, 1/t, other), parameters]
Type: decresing (probably in the same way as for neighborhood size)
Parameters: inital learning rate, decreasing rate
Initialization data sample / random / small random / grid / small grid
Distance function Euclidian / dot product
Unknown components unclear whether these are allowed or not
Teaching length explicit in epochs
training can be intercepted
Speed [Windows NT 4.0, 200 MHz Pentium Pro, 64 MB RAM] for ~3000 13-dim data samples, 10 epochs
training time: 4 minutes (map converged after 2nd epoch, though)
Results seemed ok
final average quantization error 1.29
final topographic error 13.5%
Efficiency comparison with SOM Toolbox for Matlab. Same computer, same data set, same training length. Training time 300s (sequential training), 30s (batch training). Final quantization error (sequential training) 0.68, final topographic error 1.1%.


Input formats Very good assortment: ascii (various kinds of formats), SPSS 6.0/7.0, MS Excel 5.0, Systat 5.0
Data handling and selection The environment offers very good tools for data handling. Specifically for SOM, just before training the following preprocessing operations were provided:
none / normalization of each component to unit variance & zero mean / shphering of each vector to unit length
Output formats Very good assortment: ascii (various kinds of formats), SPSS 6.0/7.0
Map measures none
Labeling no
Clustering by visualization
Inspection of neurons simple (inspection of weight vectors)
Clusters/map shape some kind of distance matrix (possibly mean distance to the neighbors of the unit)
Correlations component planes, only a single component shown at a time
Data projections no (it's possible to find BMU for vectors in test set, but no visualization tool for this is provided)
Markers no
The usability of the SOM was not very good. Training and preprocessing were ok, but postprocessing and map visualization was hard to do. We also ran into severe stability problems, for example when trying to link the Kohonen Network tool to Data output tools.

Other notes

  • In case of multiple classes, one map per class could be trained.
  • Data limits: 750 fields, 15000 records (32000 when "running" applications)
Monday, 09-Oct-2000 12:53:09 EEST