SClust: Dynamical clustering of symbolic data

In symbolicDA: Analysis of Symbolic Data

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)

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

Details

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

Value

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

Author(s)

Andrzej Dudek [e-mail], Justyna Wilk Department of Econometrics and Computer Science, Wroclaw University of Economics, Poland

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)

symbolicDA documentation built on June 26, 2025, 5:07 p.m.