Clustering list for hclust function

Question

Using plot(hclust(dist(x))) method, I was able to draw a cluster tree map. It works. Yet I would like to get a list of all clusters, not a tree diagram, because I have huge amount of data (like 150K nodes) and the plot gets messy.

In other words, lets say if a b c is a cluster and if d e f g is a cluster then I would like to get something like this:

1 a,b,c 2 d,e,f,g 

Please note that this is not exactly what I want to get as an "output". It is just an example. I just would like to be able to get a list of clusters instead of a tree plot It could be vector, matrix or just simple numbers that show which groups elements belong to.

How is this possible?

Answer 1

I will use the dataset available in R to demonstrate how to cut a tree into desired number of pieces. Result is a table.

Construct a hclust object.

hc <- hclust(dist(USArrests), "ave") #plot(hc) 

You can now cut the tree into as many branches as you want. For my next trick, I will split the tree into two groups. You set the number of cuts with the k parameter. See ?cutree and the use of paramter h which may be more useful to you (see cutree(hc, k = 2) == cutree(hc, h = 110)).

cutree(hc, k = 2)        Alabama         Alaska        Arizona       Arkansas     California               1              1              1              2              1        Colorado    Connecticut       Delaware        Florida        Georgia               2              2              1              1              2          Hawaii          Idaho       Illinois        Indiana           Iowa               2              2              1              2              2          Kansas       Kentucky      Louisiana          Maine       Maryland               2              2              1              2              1   Massachusetts       Michigan      Minnesota    Mississippi       Missouri               2              1              2              1              2         Montana       Nebraska         Nevada  New Hampshire     New Jersey               2              2              1              2              2      New Mexico       New York North Carolina   North Dakota           Ohio               1              1              1              2              2        Oklahoma         Oregon   Pennsylvania   Rhode Island South Carolina               2              2              2              2              1    South Dakota      Tennessee          Texas           Utah        Vermont               2              2              2              2              2        Virginia     Washington  West Virginia      Wisconsin        Wyoming               2              2              2              2              2 

Answer 2

lets say,

y<-dist(x) clust<-hclust(y) groups<-cutree(clust, k=3) x<-cbind(x,groups) 

now you will get for each record, the cluster group. You can subset the dataset as well:

x1<- subset(x, groups==1) x2<- subset(x, groups==2) x3<- subset(x, groups==3)