Number of Clusters, Noise Points, and Observations

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
#> DBSCAN clustering for 150 objects.
#> Parameters: eps = 0.7, minPts = 5
#> Using euclidean distances and borderpoints = TRUE
#> The clustering contains 2 cluster(s) and 3 noise points.
#> 
#>  0  1  2 
#>  3 50 97 
#> 
#> Available fields: cluster, eps, minPts, metric, borderPoints

ncluster(res)
#> [1] 2
nnoise(res)
#> [1] 3
nobs(res)
#> [1] 150

# the functions also work with kmeans and other clustering algorithms.
cl <- kmeans(iris, centers = 3)
ncluster(cl)
#> [1] 3
nnoise(cl)
#> [1] 0
nobs(res)
#> [1] 150