MinimaxLinkageClustering function

Minimax Linkage Hierarchical Clustering

Minimax Linkage Hierarchical Clustering

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

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

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 for ClusterNo=0: NULL

  • Dendrogram: Dendrogram of hierarchical clustering algorithm

  • Object: Ultrametric tree of hierarchical clustering algorithm

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.

Author(s)

Michael Thrun

See Also

HierarchicalClustering

Examples

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