This R package provides a fast C++ (re)implementation of several density-based algorithms with a focus on the DBSCAN family for clustering spatial data. The package includes:
Clustering
Outlier Detection
Fast Nearest-Neighbor Search (using kd-trees)
The implementations use the kd-tree data structure (from library ANN)
for faster k-nearest neighbor search, and are typically faster than the
native R implementations (e.g., dbscan in package fpc
), or the
implementations in WEKA,
ELKI and Python’s
scikit-learn.
Stable CRAN version: Install from within R with
install.packages("dbscan")
Current development version: Install from r-universe.
install.packages("dbscan", repos = "https://mhahsler.r-universe.dev")
Load the package and use the numeric variables in the iris dataset
library("dbscan")
data("iris")
x <- as.matrix(iris[, 1:4])
DBSCAN
db <- dbscan(x, eps = 0.4, minPts = 4)
db
## DBSCAN clustering for 150 objects.
## Parameters: eps = 0.4, minPts = 4
## Using euclidean distances and borderpoints = TRUE
## The clustering contains 4 cluster(s) and 25 noise points.
##
## 0 1 2 3 4
## 25 47 38 36 4
##
## Available fields: cluster, eps, minPts, dist, borderPoints
Visualize the resulting clustering (noise points are shown in black).
pairs(x, col = db$cluster + 1L)
OPTICS
opt <- optics(x, eps = 1, minPts = 4)
opt
## OPTICS ordering/clustering for 150 objects.
## Parameters: minPts = 4, eps = 1, eps_cl = NA, xi = NA
## Available fields: order, reachdist, coredist, predecessor, minPts, eps,
## eps_cl, xi
Extract DBSCAN-like clustering from OPTICS and create a reachability plot (extracted DBSCAN clusters at eps_cl=.4 are colored)
opt <- extractDBSCAN(opt, eps_cl = 0.4)
plot(opt)
HDBSCAN
hdb <- hdbscan(x, minPts = 4)
hdb
## HDBSCAN clustering for 150 objects.
## Parameters: minPts = 4
## The clustering contains 2 cluster(s) and 0 noise points.
##
## 1 2
## 100 50
##
## Available fields: cluster, minPts, coredist, cluster_scores,
## membership_prob, outlier_scores, hc
Visualize the hierarchical clustering as a simplified tree. HDBSCAN finds 2 stable clusters.
plot(hdb, show_flat = TRUE)
The dbscan package is licensed under the GNU General Public License (GPL) Version 3. The OPTICSXi R implementation was directly ported from the ELKI framework’s Java implementation (GNU AGPLv3), with explicit permission granted by the original author, Erich Schubert.
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