| DClust {symbolicDA} | R Documentation |
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 andrzej.dudek@ue.wroc.pl, Justyna Wilk justyna.wilk@ue.wroc.pl Department of Econometrics and Computer Science, Wroclaw University of Economics, Poland http://keii.ue.wroc.pl/symbolicDA/
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)