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.**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.

**Outlier Detection**

**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

Run DBSCAN

```
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)

Calculate LOF (local outlier factor) and visualize (larger bubbles in the visualization have a larger LOF)

Run OPTICS

```
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)

Extract a hierarchical clustering using the Xi method (captures clusters of varying density)

Run HDBSCAN (captures stable clusters)

```
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

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.