SClust {symbolicDA} | R Documentation |
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 andrzej.dudek@ue.wroc.pl, 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)