ncluster: Number of Clusters, Noise Points, and Observations

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

Number of Clusters, Noise Points, and Observations

Description

Extract the number of clusters or the number of noise points for a clustering. This function works with any clustering result that contains a list element named cluster with a clustering vector. In addition, nobs (see stats::nobs()) is also available to retrieve the number of clustered points.

Usage

ncluster(object, ...)

nnoise(object, ...)

Arguments

object	a clustering result object containing a cluster element.
...	additional arguments are unused.

Value

returns the number if clusters or noise points.

See Also

Other clustering functions: dbscan(), extractFOSC(), hdbscan(), jpclust(), optics(), sNNclust()

Examples

data(iris)
iris <- as.matrix(iris[, 1:4])

res <- dbscan(iris, eps = .7, minPts = 5)
res

ncluster(res)
nnoise(res)
nobs(res)

# the functions also work with kmeans and other clustering algorithms.
cl <- kmeans(iris, centers = 3)
ncluster(cl)
nnoise(cl)
nobs(res)

dbscan documentation built on Aug. 21, 2025, 5:49 p.m.