fdm2id (version 0.9.9)

MEANSHIFT: MeanShift method

Description

Run MeanShift for clustering.

Usage

MEANSHIFT(
  d,
  mskernel = "NORMAL",
  bandwidth = rep(1, ncol(d)),
  alpha = 0,
  iterations = 10,
  epsilon = 1e-08,
  epsilonCluster = 1e-04,
  ...
)

Value

The clustering (meanshift object).

Arguments

d

The dataset (matrix or data.frame).

mskernel

A string indicating the kernel associated with the kernel density estimate that the mean shift is optimizing over.

bandwidth

Used in the kernel density estimate for steepest ascent classification.

alpha

A scalar tuning parameter for normal kernels.

iterations

The number of iterations to perform mean shift.

epsilon

A scalar used to determine when to terminate the iteration of a individual query point.

epsilonCluster

A scalar used to determine the minimum distance between distinct clusters.

...

Other parameters.

See Also

meanShift, predict.meanshift

Examples

if (FALSE) {
require (datasets)
data (iris)
MEANSHIFT (iris [, -5], bandwidth = .75)
}