Description

Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Related Algorithms.

Description

A fast reimplementation of several density-based algorithms of the DBSCAN family. Includes the clustering algorithms DBSCAN (density-based spatial clustering of applications with noise) and HDBSCAN (hierarchical DBSCAN), the ordering algorithm OPTICS (ordering points to identify the clustering structure), shared nearest neighbor clustering, and the outlier detection algorithms LOF (local outlier factor) and GLOSH (global-local outlier score from hierarchies). The implementations use 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. Hahsler, Piekenbrock and Doran (2019) <doi:10.18637/jss.v091.i01>.

README.md

cran.r-project.org

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

Introduction

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

DBSCAN: Density-based spatial clustering of applications with noise (Ester et al, 1996).
HDBSCAN: Hierarchical DBSCAN with simplified hierarchy extraction (Campello et al, 2015).
OPTICS/OPTICSXi: Ordering points to identify the clustering structure clustering algorithms (Ankerst et al, 1999).
FOSC: Framework for Optimal Selection of Clusters for unsupervised and semisupervised clustering of hierarchical cluster tree (Campello et al, 2013).
Jarvis-Patrick clustering: Shared Nearest Neighbor Graph partitioning (Javis and Patrick, 1973).
SNN Clustering: Shared Nearest Neighbor Clustering (Erdoz et al, 2003).

Outlier Detection

LOF: Local outlier factor algorithm (Breunig et al, 2000).
GLOSH: Global-Local Outlier Score from Hierarchies algorithm (Campello et al, 2015).

Fast Nearest-Neighbor Search (using kd-trees)

kNN search
Fixed-radius NN search

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},
}

Installation

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")

Usage

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)

Using dbscan from Python

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

# get the cluster assignment vector
labels = np.array(db.rx('cluster'))
labels

## 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)

License

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.

Changes

List of changes from NEWS.md

References

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.
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), 226-231. https://dl.acm.org/doi/10.5555/3001460.3001507
Breunig, M., Kriegel, H., Ng, R., and Sander, J. (2000). LOF: identifying density-based local outliers. In ACM Int. Conf. on Management of Data, pages 93-104. doi: https://doi.org/10.1145/335191.335388
Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel, Joerg Sander (1999). OPTICS: Ordering Points To Identify the Clustering Structure. ACM SIGMOD international conference on Management of data. ACM Press. pp 49-60. doi: https://doi.org/10.1145/304181.304187
Campello, Ricardo JGB, Davoud Moulavi, Arthur Zimek, and Joerg Sander (2013). A framework for semi-supervised and unsupervised optimal extraction of clusters from hierarchies. Data Mining and Knowledge Discovery 27(3): 344-371. doi: https://doi.org/10.1007/s10618-013-0311-4
Campello RJGB, Moulavi D, Zimek A, Sander J (2015). Hierarchical density estimates for data clustering, visualization, and outlier detection. ACM Transactions on Knowledge Discovery from Data (TKDD), 10(5):1-51. doi: https://doi.org/10.1145/2733381
R. A. Jarvis and E. A. Patrick. 1973. Clustering Using a Similarity Measure Based on Shared Near Neighbors. IEEE Trans. Comput. 22, 11 (November 1973), 1025-1034. doi: https://doi.org/10.1109/T-C.1973.223640
Levent Ertoz, Michael Steinbach, Vipin Kumar, Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data, SIAM International Conference on Data Mining, 2003, 47-59. doi: https://doi.org/10.1137/1.9781611972733.5

r-dbscan

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

Introduction

Installation

Usage

Using dbscan from Python

License

Changes

References

Version

License

Status

Source

Homepage

Platforms (75)

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

Introduction

Installation

Usage

Using dbscan from Python

License

Changes

References

Version

License

Status

Source

Homepage

Platforms75 (75)

Platforms (75)