LargeApplicationClustering: Large Application Clustering

In Mthrun/FCPS: Fundamental Clustering Problems Suite

Large Application Clustering

Description

Clustering Large Applications (clara) of [Rousseeuw/Kaufman, 1990, pp. 126-163]

Usage

LargeApplicationClustering(Data, ClusterNo,

PlotIt=FALSE,Standardization=TRUE,Samples=50,Random=TRUE,...)

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.
ClusterNo	A number k which defines k different clusters to be built by the algorithm.
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
Standardization	Data is standardized before calculating the dissimilarities. Measurements are standardized for each variable (column), by subtracting the variable's mean value and dividing by the variable's mean absolute deviation.
Samples	Integer, say N, the number of samples to be drawn from the dataset. Default value set as recommended by documentation of clara
Random	Logical indicating if R's random number generator should be used instead of the primitive clara()-builtin one.
...	Further arguments to be set for the clustering algorithm, if not set, default arguments are used.

Details

It is recommended to use set.seed if clustering output should be always the same instead of setting Random=FALSE in order to use the primitive clara()-builtin random number generator.

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

[Rousseeuw/Kaufman, 1990] Rousseeuw, P. J., & Kaufman, L.: Finding groups in data, Belgium, John Wiley & Sons Inc., ISBN: 0471735787, doi 10.1002/9780470316801, Online ISBN: 9780470316801, 1990.

Examples

data('Hepta')
out=LargeApplicationClustering(Hepta$Data,ClusterNo=7,PlotIt=FALSE)

Mthrun/FCPS documentation built on June 28, 2023, 9:29 a.m.