ksharp

R package for cluster sharpening

 

Cluster sharpening is a procedure that adjusts an existing clustering in order to increase contrast between groups. In the example below, a dataset is sharpened at various levels.

Package ksharp implements a general interface for cluster sharpening along with several algorithms. The package is compatible with clusterings produced by k-means, pam, dbscan, as well as cluster definitions produced manually.

Installation

The package can be installed from CRAN.

install.packages("ksharp")

A development versian can be installed from this repository through devtools.

library(devtools)
install_github("tkonopka/ksharp")

Example

For a minimal example, consider a kmeans clustering of the iris dataset.

iris.data = as.matrix(iris[, 1:4])
rownames(iris.data) = paste0("iris_", seq_len(nrow(iris.data)))
iris.clustered = kmeans(iris.data, centers=3)
table(iris.clustered$cluster)

By construction, the dataset is partitioned into three components, labeled 1, 2, 3. Sharpening involves amending the result by shifting some of the observation into a new group with index 0.

library(ksharp)
iris.sharp.5 = ksharp(iris.clustered, threshold=0.05, data=iris.data)
table(iris.sharp.5$cluster)
iris.sharp.10 = ksharp(iris.clustered, threshold=0.1, data=iris.data)
table(iris.sharp.10$cluster)

See help(ksharp) for further usage settings. See the package vignette provides background information on cluster sharpening and further examples.