MinimaxLinkageClustering: Minimax Linkage Hierarchical Clustering

In FCPS: Fundamental Clustering Problems Suite

Minimax Linkage Hierarchical Clustering

Description

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

Usage

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

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

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