dbscan - Density Based Clustering of Applications with Noise (DBSCAN) and Related Algorithms - R package

This R package provides a fast C++ reimplementation of several density-based algorithms of the DBSCAN family for spatial data. Includes the DBSCAN (density-based spatial clustering of applications with noise) and OPTICS (ordering points to identify the clustering structure) clustering algorithms and the LOF (local outlier factor) algorithm. The implementations uses the kd-tree data structure (from library ANN) for faster k-nearest neighbor search. An R interface to fast kNN and fixed-radius NN search is also provided.

This implementation is typically faster than the native R implementation in package fpc, or the implementations in WEKA, ELKI and Python's scikit-learn.

Installation

• Stable CRAN version: install from within R.
• Current development version: Download package from AppVeyor or install via intall_github("mhahsler/dbscan") (requires devtools)

Examples

library("dbscan")

## use the numeric variables in the iris dataset
data("iris")
x <- as.matrix(iris[, 1:4])
 
## DBSCAN
db <- dbscan(x, eps = .4, minPts = 4)
db
## visualize results (noise is shown in black)
pairs(x, col = db$cluster + 1L)

## LOF (local outlier factor) 
lof <- lof(x, k = 4)
## larger bubbles in the visualization have a larger LOF
pairs(x, cex = lof)

## OPTICS
opt <- optics(x, eps = 1, minPts = 4, eps_cl = .4)
opt
## create a reachability plot (extracted DBSCAN clusters at eps_cl=.4 are colored)
plot(opt)
## plot the extracted DBSCAN clustering
pairs(x, col = opt$cluster + 1L)