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.
The following R packages use dbscan
: AFM, bioregion, CIDER, CLONETv2, ClustAssess, cordillera, CPC, crosshap, daltoolbox, DDoutlier, diceR, dobin, doc2vec, dPCP, EHRtemporalVariability, eventstream, FCPS, fdacluster, FORTLS, funtimes, FuzzyDBScan, ksharp, ktaucenters, LOMAR, maotai, metaCluster, mlr3cluster, MOSS, oclust, openSkies, opticskxi, pagoda2, parameters, ParBayesianOptimization, performance, pguIMP, rMultiNet, seriation, sfdep, sfnetworks, sharp, shipunov, smotefamily, snap, spdep, spNetwork, squat, ssMRCD, stream, supc, synr, tidySEM, ts2net
Please cite the use of this package as:
To cite dbscan in publications use:
Hahsler M, Piekenbrock M, Doran D (2019). “dbscan: Fast Density-Based Clustering with R.” Journal of Statistical Software, 91(1), 1-30. doi:10.18637/jss.v091.i01 https://doi.org/10.18637/jss.v091.i01.
@Article{,
title = {{dbscan}: Fast Density-Based Clustering with {R}},
author = {Michael Hahsler and Matthew Piekenbrock and Derek Doran},
journal = {Journal of Statistical Software},
year = {2019},
volume = {91},
number = {1},
pages = {1--30},
doi = {10.18637/jss.v091.i01},
}
Stable CRAN version: Install from within R with
Current development version: Install from r-universe.
Load the package and use the numeric variables in the iris dataset
DBSCAN
## 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).
OPTICS
## 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)
HDBSCAN
## 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.
R, R package dbscan
, and Python package rpy2
need to be installed.
import pandas as pd
import numpy as np
### prepare data
iris = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data',
header = None,
names = ['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth', 'Species'])
iris_numeric = iris[['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth']]
# get R dbscan package
from rpy2.robjects import packages
dbscan = packages.importr('dbscan')
# enable automatic conversion of pandas dataframes to R dataframes
from rpy2.robjects import pandas2ri
pandas2ri.activate()
db = dbscan.dbscan(iris_numeric, eps = 0.5, MinPts = 5)
print(db)
## DBSCAN clustering for 150 objects.
## Parameters: eps = 0.5, minPts = 5
## Using euclidean distances and borderpoints = TRUE
## The clustering contains 2 cluster(s) and 17 noise points.
##
## 0 1 2
## 17 49 84
##
## Available fields: cluster, eps, minPts, dist, borderPoints
## array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
## 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,
## 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 0, 2, 2, 2, 2, 2,
## 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0,
## 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 0, 0, 2, 0, 0,
## 2, 2, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0,
## 2, 2, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]],
## dtype=int32)
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 permission by the original author, Erich Schubert.