# clusterDBSCAN.estimateEpsilon

## Syntax

## Description

returns an estimate of the neighborhood clustering threshold, `epsilon`

= clusterDBSCAN.estimateEpsilon(`X`

,`MinNumPoints`

,`MaxNumPoints`

)`epsilon`

,
used in the density-based spatial clustering of applications with noise (DBSCAN)algorithm.
`epsilon`

is computed from input data `X`

using a
*k*-nearest neighbor (*k*-NN) search.
`MinNumPoints`

and `MaxNumPoints`

set a range of
*k*-values for which epsilon is calculated. The range extends from
`MinNumPoints`

– 1 through `MaxNumPoints`

– 1.
*k* is the number of neighbors of a point, which is one less than the
number of points in a neighborhood.

`clusterDBSCAN.estimateEpsilon(`

displays a figure showing the `X`

,`MinNumPoints`

,`MaxNumPoints`

)*k*-NN search curves and the estimated
epsilon.

## Examples

## Input Arguments

## Output Arguments

## Algorithms

## Extended Capabilities

## Version History

**Introduced in R2021a**