symbolicDA (version 0.7-1)

SClust: Dynamical clustering of symbolic data

Description

Dynamical clustering of symbolic data based on symbolic data table

Usage

SClust(table.Symbolic, cl, iter=100, variableSelection=NULL, objectSelection=NULL)

Value

a vector of integers indicating the cluster to which each object is allocated

Arguments

table.Symbolic

symbolic data table

cl

number of clusters or vector with initial prototypes of clusters

iter

maximum number of iterations

variableSelection

vector of numbers of variables to use in clustering procedure or NULL for all variables

objectSelection

vector of numbers of objects to use in clustering procedure or NULL for all objects

Author

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

Details

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

References

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. 185-191.

Verde, R. (2004), Clustering Methods in Symbolic Data Analysis, In: D. Banks, L. House, E. R. McMorris, P. Arabie, W. Gaul (Eds.), Classification, clustering and Data mining applications, Springer-Verlag, Heidelberg, pp. 299-317.

Diday, E. (1971), La methode des Nuees dynamiques, Revue de Statistique Appliquee, Vol. 19-2, pp. 19-34.

Celeux, G., Diday, E., Govaert, G., Lechevallier, Y., Ralambondrainy, H. (1988), Classifcation Automatique des Donnees, Environnement Statistique et Informatique - Dunod, Gauthier-Villards, Paris.

See Also

DClust; kmeans in stats library

Examples

# LONG RUNNING - UNCOMMENT TO RUN
#data("cars",package="symbolicDA")
#sdt<-cars
#clust<-SClust(sdt, cl=3, iter=50)
#print(clust)