# dbscan: Density Based Clustering of Applications with Noise (DBSCAN) and Related Algorithms ``````> library(dbscan)
``````

バージョン: 1.1.1

.
DS3 DS3: Spatial data with arbitrary shapes
NN Nearest Neighbors Auxiliary Functions
dbscan DBSCAN
extractFOSC Framework for Optimal Selection of Clusters
frNN Find the Fixed Radius Nearest Neighbors
glosh Global-Local Outlier Score from Hierarchies
hdbscan HDBSCAN
hullplot Plot Convex Hulls of Clusters
jpclust Jarvis-Patrick Clustering
kNN Find the k Nearest Neighbors
kNNdist Calculate and plot the k-Nearest Neighbor
Distance
lof Local Outlier Factor Score
moons Moons Data
optics OPTICS
pointdensity Calculate Local Density at Each Data Point
reachability Density Reachability Structures
sNN Shared Nearest Neighbors
sNNclust Shared Nearest Neighbor Clustering

`dbscan` DBSCAN
`frNN` Find the Fixed Radius Nearest Neighbors
`hullplot` Plot Convex Hulls of Clusters
`jpclust` Jarvis-Patrick Clustering
`kNN` Find the k Nearest Neighbors
`kNNdist` Calculate and plot the k-Nearest Neighbor Distance
`lof` Local Outlier Factor Score
`optics` OPTICS
`reachability` Density Reachability Structures

``````> set.seed(71)
> n <- 100
> x <- cbind(
+   x = runif(10, 0, 10) + rnorm(n, sd = 0.2),
+   y = runif(10, 0, 10) + rnorm(n, sd = 0.2)
+   )
>
> (res <- dbscan(x, eps = .3, minPts = 3))
``````
``````DBSCAN clustering for 100 objects.
Parameters: eps = 0.3, minPts = 3
The clustering contains 9 cluster(s) and 11 noise points.

0  1  2  3  4  5  6  7  8  9
11 20 10 10 10  8  8  4  9 10

Available fields: cluster, eps, minPts
``````