kohonen.SDA: Kohonen's self-organizing maps for symbolic interval-valued...

View source: R/kohonen.SDA.r

kohonen.SDAR Documentation

Kohonen's self-organizing maps for symbolic interval-valued data

Description

Kohonen's self-organizing maps for a set of symbolic objects described by interval-valued variables

Usage

kohonen.SDA(data, rlen=100, alpha=c(0.05,0.01))

Arguments

data

symbolic data table in simple form (see SO2Simple)

rlen

number of iterations (the number of times the complete data set will be presented to the network)

alpha

learning rate, determining the size of the adjustments during training. Default is to decline linearly from 0.05 to 0.01 over rlen updates

Details

See file ../doc/kohonenSDA_details.pdf for further details

Value

clas

vector of mini-class belonginers in a test set

prot

prototypes

Author(s)

Andrzej Dudek andrzej.dudek@ue.wroc.pl, Justyna Wilk justyna.wilk@ue.wroc.pl Department of Econometrics and Computer Science, Wroclaw University of Economics, Poland http://keii.ue.wroc.pl/symbolicDA/

References

Kohonen, T. (1995), Self-Organizing Maps, Springer, Berlin-Heidelberg.

Bock, H.H. (2001), Clustering Algorithms and Kohonen Maps for Symbolic Data, International Conference on New Trends in Computational Statistics with Biomedical Applications, ICNCB Proceedings, Osaka, pp. 203-215.

Bock, H.H., Diday, E. (eds.) (2000), Analysis of Symbolic Data. Explanatory Methods for Extracting Statistical Information from Complex Data, Springer-Verlag, Berlin.

Diday, E., Noirhomme-Fraiture, M. (eds.) (2008), Symbolic Data Analysis with SODAS Software, John Wiley & Sons, Chichester, pp. 373-392.

See Also

SO2Simple; som in kohonen library

Examples

# Example will be available in next version of package, thank You for your patience :-)

symbolicDA documentation built on May 28, 2022, 1:08 a.m.