HCLclustering: On-line Update (Hard Competitive learning) method

In FCPS: Fundamental Clustering Problems Suite

On-line Update (Hard Competitive learning) method

Description

Hard Competitive learning clustering published by [Ripley, 2007].

Usage

HCLclustering(Data, ClusterNo,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.
ClusterNo	A number k which defines k different clusters to be build 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
...	Further arguments to be set for the clustering algorithm, if not set, default arguments are used.

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

[Dimitriadou, 2002] Dimitriadou, E.: cclust-convex clustering methods and clustering indexes. R package, 2002,

[Ripley, 2007] Ripley, B. D.: Pattern recognition and neural networks, Cambridge university press, ISBN: 0521717701, 2007.

Examples

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

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