hclustPH: Single linkage clustering using persistent homology

In arnhew99/hclustPH: Single Linkage Clustering Using Persistent Homology

Description

Hiearchical cluster analysis using the single linkage agglomeration method, computed using persistent homology. This method avoids computing the distance matrix directly.

Usage

hclustPH(X)

Arguments

X	Data matrix with observations as rows and column dimension of 8 or less.

Value

An object of class hclust which describes the tree produced by the clustering process. The object is a list with components:

merge	an n-1 by 2 matrix. Row i of merge describes the merging of clusters at step i of the clustering. If an element j in the row is negative, then observation -j was merged at this stage. If j is positive then the merge was with the cluster formed at the (earlier) stage j of the algorithm. Thus negative entries in merge indicate agglomerations of singletons, and positive entries indicate agglomerations of non-singletons.
height	a set of n-1 real values (non-decreasing for ultrametric trees). The clustering height: that is, the value of the criterion associated with the single linkage method for the particular agglomeration.
order	a vector giving the permutation of the original observations suitable for plotting, in the sense that a cluster plot using this ordering and matrix merge will not have crossings of the branches.
labels	Currently NULL
call	Currently NULL
method	Currently NULL

Author(s)

Matthew Arnold

Examples

X <- matrix(runif(1000), ncol=2)
clu <- hclustPH(X)

arnhew99/hclustPH documentation built on May 10, 2019, 1:45 p.m.