hclust() in R on large datasets

I am trying implement hierarchical clustering in R : hclust() ; this requires a distance matrix created by dist() but my dataset has around a million rows, and even EC2 instances run out of RAM. Is there a workaround?

One possible solution for this is to sample your data, cluster the smaller sample, then treat the clustered sample as training data for k Nearest Neighbors and "classify" the rest of the data. Here is a quick example with 1.1M rows. I use a sample of 5000 points. The original data is not well-separated, but with only 1/220 of the data, the sample is separated. Since your question referred to hclust, I used that. But you could use other clustering algorithms like dbscan or mean shift.

## Generate data
set.seed(2017)
x = c(rnorm(250000, 0,0.9), rnorm(350000, 4,1), rnorm(500000, -5,1.1))
y = c(rnorm(250000, 0,0.9), rnorm(350000, 5.5,1), rnorm(500000,  5,1.1))
XY = data.frame(x,y)
Sample5K = sample(length(x), 5000)     ## Downsample

## Cluster the sample
DM5K = dist(XY[Sample5K,])
HC5K = hclust(DM5K, method="single")
Groups = cutree(HC5K, 8)
Groups[Groups>4] = 4
plot(XY[Sample5K,], pch=20, col=rainbow(4, alpha=c(0.2,0.2,0.2,1))[Groups])

Now just assign all other points to the nearest cluster.

Core = which(Groups<4)
library(class)
knnClust = knn(XY[Sample5K[Core], ], XY, Groups[Core])
plot(XY, pch=20, col=rainbow(3, alpha=0.1)[knnClust])

A few quick notes.

Because I created the data, I knew to choose three clusters. With a real problem, you would have to do the work of figuring out an appropriate number of clusters.
Sampling 1/220 could completely miss any small clusters. In the small sample, they would just look like noise.