DivisiveAnalysisClustering {FCPS}

Large DivisiveAnalysisClustering Clustering

Description

Divisive Analysis Clustering (diana) of [Rousseeuw/Kaufman, 1990, pp. 253-279]

Usage

DivisiveAnalysisClustering(DataOrDistances, ClusterNo,

PlotIt=FALSE,Standardization=TRUE,PlotTree=FALSE,Data,...)

Arguments

DataOrDistances

[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. Alternatively, symmetric [1:n,1:n] distance matrix

ClusterNo

A number k which defines k different clusters to be build by the algorithm. if ClusterNo=0 and PlotTree=TRUE, the dendrogram is generated instead of a clustering to estimate the numbers of clusters.

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

DataOrDistances 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.If DataOrDistances Is already a distance matrix, then this argument will be ignored.

PlotTree

TRUE: Plots the dendrogram, FALSE: no plot

Data

[1:n,1:d] data matrix in the case that DataOrDistances is missing and partial matching does not work.

...

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.

Dendrogram

Dendrogram of hierarchical clustering algorithm

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')
CA=DivisiveAnalysisClustering(Hepta$Data,ClusterNo=7,PlotIt=FALSE)

print(CA$Object)
plot(CA$Object)
ClusterDendrogram(CA$Dendrogram,7,main='DIANA')