HierarchicalClusterDists function

Internal Function of Hierarchical Clustering with Distances

Internal Function of Hierarchical Clustering with Distances

Please use HierarchicalClustering. Cluster analysis on a set of dissimilarities and methods for analyzing it. Uses stats package function 'hclust'.

HierarchicalClusterDists(pDist,ClusterNo=0,Type="ward.D2", ColorTreshold=0,Fast=FALSE,...)

Arguments

  • pDist: Distances as either matrix [1:n,1:n] or dist object
  • ClusterNo: A number k which defines k different clusters to be built by the algorithm.
  • Type: Method of cluster analysis: "ward.D", "ward.D2", "single", "complete", "average", "mcquitty", "median" or "centroid".
  • ColorTreshold: Draws cutline w.r.t. dendogram y-axis (height), height of line as scalar should be given
  • Fast: If TRUE and fastcluster installed, then a faster implementation of the methods above can be used
  • ...: 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

Author(s)

Michael Thrun

Examples

data('Hepta') #out=HierarchicalClusterDists(as.matrix(dist(Hepta$Data)),ClusterNo=7)

See Also

HierarchicalClusterData

HierarchicalClusterDists

HierarchicalClustering

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

Downloads (last 30 days):