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 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)