search for: clstructur

...ers, color=TRUE, shade=TRUE,labels=4, lines=0) #visualization of the clusters in 2D map var1<-var(clusters==1) #variance of cluster 1 #extract cluster memberships: clids = as.data.frame(clusters) names(clids) = c("id") clids$cdr = row.names(clids) row.names(clids) = c(1:dim(clids)[1]) clstructure = lapply(unique(clids$id), function(x){clids[clids$id == x,'cdr']}) clstructure[[1]] #get memberships of cluster 1 >From this point, eventually, I could recreate a distance matrix with only the members of a specific cluster and then re-apply hierarchical clustering and start all ove...