Density Based Clustering of Applications with Noise (DBSCAN) and Related Algorithms
A fast 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 HDBSCAN (hierarchical DBSCAN) 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.
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. The package includes:
- DBSCAN: Density-based spatial clustering of applications with noise.
- OPTICS/OPTICSXi: Ordering points to identify the clustering structure clustering algorithms.
- HDBSCAN: Hierarchical DBSCAN with simplified hierarchy extraction.
- LOF: Local outlier factor algorithm.
- GLOSH: Global-Local Outlier Score from Hierarchies 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 provided along with Jarvis-Patrick clustering and Shared Nearest Neighbor Clustering. Additionally, a fast implementation of the Framework for Optimal Selection of Clusters (FOSC) is available that supports unsupervised and semisupervised clustering of hierarchical cluster tree ('hclust' object). Supports any arbitrary linkage criterion.
Stable CRAN version: install from within R with
Current development version: Download package from AppVeyor or install from GitHub (needs devtools).
Load the package and use the numeric variables in the iris dataset
library("dbscan") data("iris") x <- as.matrix(iris[, 1:4])
db <- dbscan(x, eps = .4, minPts = 4) db
DBSCAN clustering for 150 objects. Parameters: eps = 0.4, minPts = 4 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
Visualize results (noise is shown in black)
pairs(x, col = db$cluster + 1L)
Calculate LOF (local outlier factor) and visualize (larger bubbles in the visualization have a larger LOF)
lof <- lof(x, k = 4) pairs(x, cex = lof)
opt <- optics(x, eps = 1, minPts = 4) opt
OPTICS clustering for 150 objects. Parameters: minPts = 4, eps = 1, eps_cl = NA, xi = NA Available fields: order, reachdist, coredist, predecessor, minPts, eps, eps_cl, xi
Extract DBSCAN-like clustering from OPTICS and create a reachability plot (extracted DBSCAN clusters at eps_cl=.4 are colored)
opt <- extractDBSCAN(opt, eps_cl = .4) plot(opt)
Extract a hierarchical clustering using the Xi method (captures clusters of varying density)
opt <- extractXi(opt, xi = .05) opt plot(opt)
Run HDBSCAN (captures stable clusters)
hdb <- hdbscan(x, minPts = 4) hdb
HDBSCAN clustering for 150 objects. Parameters: minPts = 4 The clustering contains 2 cluster(s) and 0 noise points. 1 2 100 50 Available fields: cluster, minPts, cluster_scores, membership_prob, outlier_scores, hc
Visualize the results as a simplified tree
plot(hdb, show_flat = T)
See how well each point corresponds to the clusters found by the model used
colors <- mapply(function(col, i) adjustcolor(col, alpha.f = hdb$membership_prob[i]), palette()[hdb$cluster+1], seq_along(hdb$cluster)) plot(x, col=colors, pch=20)
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.
Maintainer: Michael Hahsler
Functions in dbscan
|kNN||Find the k Nearest Neighbors|
|pointdensity||Calculate Local Density at Each Data Point|
|lof||Local Outlier Factor Score|
|kNNdist||Calculate and plot the k-Nearest Neighbor Distance|
|sNN||Shared Nearest Neighbors|
|sNNclust||Shared Nearest Neighbor Clustering|
|DS3||DS3: Spatial data with arbitrary shapes|
|NN||Nearest Neighbors Auxiliary Functions|
|extractFOSC||Framework for Optimal Selection of Clusters|
|glosh||Global-Local Outlier Score from Hierarchies|
|reachability||Density Reachability Structures|
|frNN||Find the Fixed Radius Nearest Neighbors|
|hullplot||Plot Convex Hulls of Clusters|
Vignettes of dbscan
Last month downloads
|License||GPL (>= 2)|
|Copyright||ANN library is copyright by University of Maryland, Sunil Arya and David Mount. All other code is copyright by Michael Hahsler and Matthew Piekenbrock.|
|Packaged||2017-03-19 17:57:07 UTC; hahsler|
|Date/Publication||2017-03-19 23:26:00 UTC|
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