MinimalEnergyClustering {FCPS}

Minimal Energy Clustering

Description

Hierchical Clustering using the minimal energy approach of [Szekely/Rizzo, 2005].

Usage

MinimalEnergyClustering(DataOrDistances, ClusterNo = 0,
DistanceMethod="euclidean", ColorTreshold = 0,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.

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

Data

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

...

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 ClusterNo=0: NULL

Dendrogram

Dendrogram of hierarchical clustering algorithm

Object

Ultrametric tree of hierarchical clustering algorithm

Author(s)

Michael Thrun

References

[Szekely/Rizzo, 2005] Szekely, G. J. and Rizzo, M. L.: Hierarchical Clustering via Joint Between-Within Distances: Extending Ward's Minimum Variance Method, Journal of Classification, 22(2) 151-183.http://dx.doi.org/10.1007/s00357-005-0012-9, 2005.

See Also

HierarchicalClustering

Examples

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