kNNdist  R Documentation 
Fast calculation of the knearest neighbor distances for a dataset
represented as a matrix of points. The kNN distance is defined as the
distance from a point to its k nearest neighbor. The kNN distance plot
displays the kNN distance of all points sorted from smallest to largest. The
plot can be used to help find suitable parameter values for dbscan()
.
kNNdist(x, k, all = FALSE, ...)
kNNdistplot(x, k, minPts, ...)
x 
the data set as a matrix of points (Euclidean distance is used) or a precalculated dist object. 
k 
number of nearest neighbors used for the distance calculation. For

all 
should a matrix with the distances to all k nearest neighbors be returned? 
... 
further arguments (e.g., kdtree related parameters) are passed
on to 
minPts 
to use a kNN plot to determine a suitable 
kNNdist()
returns a numeric vector with the distance to its k
nearest neighbor. If all = TRUE
then a matrix with k columns
containing the distances to all 1st, 2nd, ..., kth nearest neighbors is
returned instead.
Michael Hahsler
Other Outlier Detection Functions:
glosh()
,
lof()
,
pointdensity()
Other NN functions:
NN
,
comps()
,
frNN()
,
kNN()
,
sNN()
data(iris)
iris < as.matrix(iris[, 1:4])
## Find the 4NN distance for each observation (see ?kNN
## for different search strategies)
kNNdist(iris, k = 4)
## Get a matrix with distances to the 1st, 2nd, ..., 4th NN.
kNNdist(iris, k = 4, all = TRUE)
## Produce a kNN distance plot to determine a suitable eps for
## DBSCAN with MinPts = 5. Use k = 4 (= MinPts 1).
## The knee is visible around a distance of .7
kNNdistplot(iris, k = 4)
## Look at all kNN distance plots for a k of 1 to 10
## Note that kNN distances are increasing in k
kNNdistplot(iris, k = 1:20)
cl < dbscan(iris, eps = .7, minPts = 5)
pairs(iris, col = cl$cluster + 1L)
## Note: black points are noise points
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.