MeanShiftClustering: Mean Shift Clustering

In FCPS: Fundamental Clustering Problems Suite

Mean Shift Clustering

Description

Mean Shift Clustering of [Cheng, 1995]

Usage

MeanShiftClustering(Data,

PlotIt=FALSE,...)

Arguments

Data	[1:n,1:d] matrix of dataset to be clustered. It consists of n cases of d-dimensional data points. Every case has d attributes, variables or features.
PlotIt	Default: FALSE, If TRUE plots the first three dimensions of the dataset with colored three-dimensional data points defined by the clustering stored in Cls
...	Further arguments to be set for the clustering algorithm, if not set, default arguments are used.

Details

the radius used for search can be specified with the "radius" parameter. The maximum number of iterations before algorithm termination is controlled with the "max_iterations" parameter.

If the distance between two centroids is less than the given radius, one will be removed. A radius of 0 or less means an estimate will be calculated and used for the radius. Default value "0" (numeric).

Value

List of

Cls	[1:n] numerical vector with n numbers defining the classification as the main output of the clustering algorithm. It has k unique numbers representing the arbitrary labels of the clustering.
Object	Object defined by clustering algorithm as the other output of this algorithm

Author(s)

Michael Thrun

References

[Cheng, 1995] Cheng, Yizong: Mean Shift, Mode Seeking, and Clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 17 (8), pp. 790-799, doi:10.1109/34.400568, 1995.

Examples

data('Hepta')
out=MeanShiftClustering(Hepta$Data,PlotIt=FALSE,radius=1)

FCPS documentation built on Oct. 19, 2023, 5:06 p.m.