R Programming/Clustering

Basic clustering

You can use the kmeans() function.

First create some data:

> dat <- matrix(rnorm(100), nrow=10, ncol=10)

To apply kmeans(), you need to specify the number of clusters:

> cl <- kmeans(dat, 3) # here 3 is the number of clusters > table(cl$cluster)  1 2 3  38 44 18

The basic hierarchical clustering function is hclust(), which works on a dissimilarity structure as produced by the dist() function:

> hc <- hclust(dist(dat)) # data matrix from the example above > plot(hc)

The resulting tree can be cut using the cutree() function.

Cutting it at a given height:

> cl <- cutree(hc, h=5.1) > table(cl) cl  1 2 3 4 5  23 33 29 4 11

Cutting it to obtain given number of clusters:

> cl <- cutree(hc, k=5) > table(cl) cl  1 2 3 4 5  23 33 29 4 11