banditpam is an R package that lets you do \(k\)-mediods clustering efficiently as
described in Tiwari, et. al. (2020).
We illustrate with a simple example using simulated data from a Gaussian Mixture Model with the the following means: \((0, 0)\), \((-5, 5)\) and \((5, 5)\).
set.seed(10) n_per_cluster <- 40 means <- list(c(0, 0), c(-5, 5), c(5, 5)) X <- do.call(rbind, lapply(means, MASS::mvrnorm, n = n_per_cluster, Sigma = diag(2)))
Let’s cluster the observations in this
X matrix using 3
clusters. The first step is to create a
Next we fit the data with a specified loss, \(l_2\) here. A good habit is to set the seed before fitting for reproducibility.
And we can now extract the medoid observation indices.
A plot shows the results where we color the medoids in red.
d <- as.data.frame(X); names(d) <- c("x", "y") dd <- d[med_indices, ] ggplot(data = d) + geom_point(aes(x, y)) + geom_point(aes(x, y), data = dd, color = "red")
We can also change the loss function and see how the mediods change.
One can query some performance statistics too; see help on