HierarchicalClusterData: Internal function of Hierarchical Clusterering of Data

In Mthrun/FCPS: Fundamental Clustering Problems Suite

Internal function of Hierarchical Clusterering of Data

Description

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

Usage

HierarchicalClusterData(Data,ClusterNo=0,

Type="ward.D2",DistanceMethod="euclidean",

ColorTreshold=0,Fast=FALSE,Cls=NULL,...)

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.
Type	Methode der Clusterung: "ward.D", "ward.D2", "single", "complete", "average", "mcquitty", "median" or "centroid".
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. dendrogram 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
Cls	[1:n] classification vector for coloring of dendrogram in plot
...	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=HierarchicalClusterData(Hepta$Data,ClusterNo=7)

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