dbscan - Density Based Clustering of Applications with Noise (DBSCAN) and Related Algorithms - R package
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:
- DBSCAN: Density-based spatial clustering of applications with noise.
- HDBSCAN: Hierarchical DBSCAN with simplified hierarchy extraction.
- OPTICS/OPTICSXi: Ordering points to identify the clustering structure clustering algorithms.
- FOSC: Framework for Optimal Selection of Clusters for unsupervised and semisupervised clustering of hierarchical cluster tree.
- Jarvis-Patrick clustering
- SNN Clustering: Shared Nearest Neighbor Clustering.
- LOF: Local outlier factor algorithm.
- GLOSH: Global-Local Outlier Score from Hierarchies algorithm.
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.
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.
- List of changes from NEWS.md
- 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.