FannyClustering {FCPS}

Fuzzy Analysis Clustering [Rousseeuw/Kaufman, 1990, p. 253-279]

Description

...

Usage

FannyClustering(DataOrDistances,ClusterNo,

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

Arguments

DataOrDistances

[1:n,1:d] matrix of dataset to be clustered. It consists of n cases or 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.

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.

...

Further arguments to be set for the clustering algorithm, if not set, default arguments are used.

Details

...

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. Points which cannot be assigned to a cluster will be reported with 0.

Object

Object defined by clustering algorithm as the second 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=FannyClustering(Hepta$Data,ClusterNo=7,PlotIt=FALSE)