fdm2id (version 1.0.1)

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,
  seed = NULL,
  ...
)

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 an individual query point.

epsilonCluster

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

seed

A specified seed for random number generation. The MeanShift algorithm itself is deterministic given its parameters, but the seed is provided for consistency with the rest of the package's API.

...

Other parameters.

See Also

meanShift, predict.meanshift

Examples

# \donttest{
require (datasets)
data (iris)
MEANSHIFT (iris [, -5], bandwidth = .75)
# }