HierarchicalClusterDists: Internal Function of Hierarchical Clustering with Distances

In Mthrun/FCPS: Fundamental Clustering Problems Suite

Internal Function of Hierarchical Clustering with Distances

Description

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

Usage

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

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 for ClusterNo=0: NULL
Dendrogram	Dendrogram of hierarchical clustering algorithm
Object	Ultrametric tree of hierarchical clustering algorithm

Author(s)

Michael Thrun

See Also

HierarchicalClusterData

HierarchicalClusterDists

HierarchicalClustering

Examples

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

Mthrun/FCPS documentation built on June 28, 2023, 9:29 a.m.