R Programming/Clustering

Basic clustering K-Means 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 Hierarchical Clustering 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 Available alternatives See packages class, amap and cluster See The R bioinformatic page on clustering References "The Elements of Statistical Learning" External links Clustergram: visualization and diagnostics for cluster analysis