HCLclustering function

On-line Update (Hard Competitive learning) method

On-line Update (Hard Competitive learning) method

Hard Competitive learning clustering published by [Ripley, 2007].

HCLclustering(Data, ClusterNo,PlotIt=FALSE,...)

Arguments

  • Data: [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.
  • ClusterNo: A number k which defines k different clusters to be build by the algorithm.
  • PlotIt: Default: FALSE, If TRUE plots the first three dimensions of the dataset with colored three-dimensional data points defined by the clustering stored in Cls
  • ...: Further arguments to be set for the clustering algorithm, if not set, default arguments are used.

Returns

List of - Cls: [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.

  • Object: Object defined by clustering algorithm as the other output of this algorithm

Examples

data('Hepta') out=HCLclustering(Hepta$Data,ClusterNo=7,PlotIt=FALSE)

Author(s)

Michael Thrun

References

[Dimitriadou, 2002] Dimitriadou, E.: cclust-convex clustering methods and clustering indexes. R package, 2002,

[Ripley, 2007] Ripley, B. D.: Pattern recognition and neural networks, Cambridge university press, ISBN: 0521717701, 2007.

  • Maintainer: Michael Thrun
  • License: GPL-3
  • Last published: 2023-10-19