dist.birch {birch}R Documentation

Create a dissimilarity matrix from a birch object

Description

This function computes and returns the distance matrix computed by passing the centers of the subclusters in the birch object to dist.

Usage

dist.birch(birchObject, ...)

Arguments

birchObject An object created by the function birch.
... Further arguments passed to dist.

Details

This function is a wrapper for the dist function, for application on the centers of birch objects. It is provided as a convenient mechanism for provided estimates of initial centers for the kmeans.birch function. An example of this is provided in the section below.

Value

An object of the class “dist”. See dist for more details.

Author(s)

Justin Harrington harringt@stat.ubc.ca and Matias Salibian-Barrera matias@stat.ubc.ca

References

Harrington, J and Salibian-Barrera, M (2007) “Finding Approximate Solutions to Combinatorial Problems with Very Large Datasets using BIRCH”, submitted to Statistical Algorithms and Software, 2nd Special Issue Computational Statistics and Data Analysis. A draft can be found at http://www.stat.ubc.ca/~harringt/birch/birch.pdf.

Harrington, J and Salibian-Barrera, M (2008) “birch: Working with very large data sets”, submitted to Journal of Statistical Software. A draft can be found at http://www.stat.ubc.ca/~harringt/birch/birch-jss.pdf.

See Also

birch, dist, kmeans.birch

Examples

## Load a demo birch Object
data(birchObj)

## Calculate the distances, perform hclust, and then
## find out which clustering corresponds to 2 groups
bDist <- dist.birch(birchObj)
hc <- hclust(bDist)
clusters <- cutree(hc, 2)

## Calculate centers based on these clusters, and cluster with them
centers <- rbind(summary(birchObj[clusters == 1,])$mean,
summary(birchObj[clusters == 2,])$mean)
kOut <- kmeans.birch(birchObj, centers)

[Package birch version 1.1-3 Index]