Clustering data with the density-based method
As an alternative to distance measurement, you can use a density-based measurement to cluster data. This method finds an area with a higher density than the remaining area. One of the most famous methods is DBSCAN. In the following recipe, we will demonstrate how to use DBSCAN to perform density-based clustering.
Getting ready
In this recipe, we will use simulated data generated from the mlbench package.
How to do it...
Perform the following steps to perform density-based clustering:
- First, install and load the
fpcandmlbenchpackages:
> install.packages("mlbench")
> library(mlbench)
> install.packages("fpc")
> library(fpc) - You can then use the
mlbenchlibrary to draw a Cassini problem graph:
> set.seed(2)
> p = mlbench.cassini(500)
> plot(p$x) 
The Cassini problem graph
- Next, you can cluster data with regard to its density measurement:
> ds = dbscan(dist(p$x),0...
