kNNdist | R Documentation |

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()`

.

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. |

`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 |

`minPts` |
to use a k-NN 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 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

dbscan documentation built on Oct. 29, 2022, 1:13 a.m.

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