hclust

Is there an easy way to calculate lowest value of h in cut that produces groupings of a given minimum size? In this example, if I wanted clusters with at least ten members each, I should go with h = 3.80 : # using iris data simply for reproducible example data(iris) d <- data.frame(scale(iris[,1:4])) hc <- hclust(dist(d)) plot(hc) cut(as.dendrogram(hc), h=3.79) # produces 5 groups; group 4 has 7 members cut(as.dendrogram(hc), h=3.80) # produces 4 groups; no group has <10 members Since the heights of the splits are given in hc$height , I could create a set of candidate values using hc$height +

I tried to construct the clustering method as function the following ways: mydata <- mtcars # Here I construct hclust as a function hclustfunc <- function(x) hclust(as.matrix(x),method="complete") # Define distance metric distfunc <- function(x) as.dist((1-cor(t(x)))/2) # Obtain distance d <- distfunc(mydata) # Call that hclust function fit<-hclustfunc(d) # Later I'd do # plot(fit) But why it gives the following error: Error in if (is.na(n) || n > 65536L) stop("size cannot be NA nor exceed 65536") : missing value where TRUE/FALSE needed What's the right way to do it? Do read the help for

I am trying implement hierarchical clustering in R : hclust() ; this requires a distance matrix created by dist() but my dataset has around a million rows, and even EC2 instances run out of RAM. Is there a workaround? One possible solution for this is to sample your data, cluster the smaller sample, then treat the clustered sample as training data for k Nearest Neighbors and "classify" the rest of the data. Here is a quick example with 1.1M rows. I use a sample of 5000 points. The original data is not well-separated, but with only 1/220 of the data, the sample is separated. Since your question

I am trying to draw a dendrogram from the hclust function output. I hope the dendrogram is horizontally arranged instead of the default, which can be obtain by (for example) require(graphics) hc <- hclust(dist(USArrests), "ave") plot(hc) I tried to use as.dendrogram() function like plot(as.dendrogram(hc.poi),horiz=TRUE) but the result is without meaningful labels: If I use plot(hc.poi,labels=c(...)) which is without the as.dendrogram() , I can pass the labels= argument, but now the dendrogram is vertical instead of horizontal. Is there a way to simultaneously arrange the dendrogram