Description Usage Arguments Value Author(s) See Also Examples

Fast calculation of the k-nearest 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()`

.

1 2 3 |

`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. |

`all` |
should a matrix with the distances to all k nearest neighbors be returned? |

`...` |
further arguments (e.g., kd-tree related parameters) are passed
on to |

`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()`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
data(iris)
iris <- as.matrix(iris[, 1:4])
## Find the 4-NN 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 k-NN 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)
cl <- dbscan(iris, eps = .7, minPts = 5)
pairs(iris, col = cl$cluster + 1L)
## Note: black points are noise points
``` |

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