DClust: Dynamical clustering based on distance matrix

In symbolicDA: Analysis of Symbolic Data

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)

Arguments

dist	distance matrix
cl	number of clusters or vector with initial prototypes of clusters
iter	maximum number of iterations

Details

See file ../doc/DClust_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. 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)

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