MinimalEnergyClustering function

Minimal Energy Clustering

Minimal Energy Clustering

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

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

Returns

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

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.

Author(s)

Michael Thrun

See Also

HierarchicalClustering

Examples

data('Hepta') out=MinimalEnergyClustering(Hepta$Data,ClusterNo=7)
  • Maintainer: Michael Thrun
  • License: GPL-3
  • Last published: 2023-10-19