I know I am probably late to this party but I recently found out about DBSCAN or “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise”[^1]. In a nutshell, the algorithm visits successive data point and asks whether neighbouring points are density-reachable. In other words is it possible to connect two points with a chain of points all conforming to some density criteria. This has some major advantages over other clustering algorithms that I have used before.
- It can identify clusters of arbitrary shape.
- Number of clusters is not an input parameter.
- It's fast as it only visits the data points rather than the space in between.
- A data point with no close neighbours is assigned noise rather than its nearest cluster.
Let have a go at clustering uk cities from
library(maps). First load the packages and the data, then subset the data to get only the UK cities.
library(ggplot2) library(dplyr) library(maps) library(dbscan) data("world.cities") UK <- world.cities %>% filter(country.etc == "UK")
Now we can run the algorithm on the latitude and longitude collumns. Then we can pull the cluster assignments out of the resulting object.
EPS <- 0.15 clusters <- dbscan(select(UK, lat, long), eps = EPS) UK$cluster <- clusters$cluster
Finally we can split the original data into two according to whether dbscan has assigned or cluster or noise.
groups <- UK %>% filter(cluster != 0) noise <- UK %>% filter(cluster == 0)
Now lets have a look at the results[^2].
ggplot(UK, aes(x = long, y = lat, alpha = 0.5)) + geom_point(aes(fill = "grey"), noise) + geom_point(aes(colour = as.factor(cluster)), groups, size = 3) + coord_map() + theme_stripped + theme_empty + theme(legend.position = "none")
I arbitrarily set the EPS parameter. How to tune it? Discussion for another time...
[^1]: I recommend reading the paper which is quite accesible. Martin Ester, Hans-Peter Kriegel, Joerg Sander, Xiaowei Xu (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Institute for Computer Science, University of Munich. Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96).
[^2]: I am stripping out some of the ggplot defaults with two objects
theme_empty which I use routinely to either remove the background and gridlines or to remove everything including axes.