symbolicDA (version 0.7-1)

DClust: Dynamical clustering based on distance matrix

Description

Dynamical clustering of objects described by symbolic and/or classic (metric, non-metric) variables based on distance matrix

Usage

DClust(dist, cl, iter=100)

Value

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

Arguments

dist

distance matrix

cl

number of clusters or vector with initial prototypes of clusters

iter

maximum number of iterations

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/DClust_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. 191-204.

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

SClust, dist_SDA; dist in stats library; dist.GDM in clusterSim library; pam in cluster library

Examples

# LONG RUNNING - UNCOMMENT TO RUN
#data("cars",package="symbolicDA")
#sdt<-cars
#dist<-dist_SDA(sdt, type="U_3")
#clust<-DClust(dist, cl=5, iter=100)
#print(clust)