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)))Letâs cluster the observations in this X matrix using 3 clusters. The first step is to create a KMedoids object:
obj <- KMedoids$new(k = 3)Next we fit the data with a specified loss, \(l_2\) here. A good habit is to set the seed before fitting for reproducibility.
set.seed(198)
obj$fit(data = X, loss = "l2")And we can now extract the medoid observation indices.
med_indices <- obj$get_medoids_final()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")Clustering with 3-mediods with L2 loss
To obtain the cluster labels of each observations, one can use the get_labels() method on the object:
head(obj$get_labels())
#> [1] 1 1 1 1 1 1
tail(obj$get_labels())
#> [1] 3 3 3 3 3 3We can also change the loss function and see how the mediods change.
obj$fit(data = X, loss = "l1") # L1 loss
med_indices <- obj$get_medoids_final()Clustering with 3-mediods with L1 loss
One can query some performance statistics too; see help on KMedoids.
obj$get_statistic("dist_computations") # no of dist computations
#> [1] 44263
obj$get_statistic("cache_misses") # no of cache misses
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