MinimaxLinkageClustering: Minimax Linkage Hierarchical Clustering

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/MinimaxLinkageClustering.R

Description

In the minimax linkage hierarchical clustering every cluster has an associated prototype element that represents that cluster [Bien/Tibshirani, 2011].

Usage

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MinimaxLinkageClustering(DataOrDistances, ClusterNo = 0,
DistanceMethod="euclidean", ColorTreshold = 0,...)

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.

DistanceMethod

See parDist, for example 'euclidean','mahalanobis','manhatten' (cityblock),'fJaccard','binary', 'canberra', 'maximum'. Any unambiguous substring can be given.

ColorTreshold

Draws cutline w.r.t. dendogram y-axis (height), height of line as scalar should be given

...

In case of plotting further argument for plot, see as.dendrogram

Value

List of

Cls

If, ClusterNo>0: [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. Otherwise for ClusterNo=0: NULL

Dendrogram

Dendrogram of hierarchical clustering algorithm

Object

Ultrametric tree of hierarchical clustering algorithm

Author(s)

Michael Thrun

References

[Bien/Tibshirani, 2011] Bien, J., and Tibshirani, R.: Hierarchical Clustering with Prototypes via Minimax Linkage, The Journal of the American Statistical Association, Vol. 106(495), pp. 1075-1084, 2011.

See Also

HierarchicalClustering

Examples

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data('Hepta')
out=MinimaxLinkageClustering(Hepta$Data,ClusterNo=7)

FCPS documentation built on July 8, 2021, 1:06 a.m.