RWeka (version 0.4-47)

Weka_clusterers: R/Weka Clusterers

Description

R interfaces to Weka clustering algorithms.

Usage

Cobweb(x, control = NULL)
FarthestFirst(x, control = NULL)
SimpleKMeans(x, control = NULL)
XMeans(x, control = NULL)
DBScan(x, control = NULL)

Value

A list inheriting from class Weka_clusterers with components including

clusterer

a reference (of class jobjRef) to a Java object obtained by applying the Weka buildClusterer method to the training instances using the given control options.

class_ids

a vector of integers indicating the class to which each training instance is allocated (the results of calling the Weka clusterInstance method for the built clusterer and each instance).

Arguments

x

an R object with the data to be clustered.

control

an object of class Weka_control, or a character vector of control options, or NULL (default). Available options can be obtained on-line using the Weka Option Wizard WOW, or the Weka documentation.

Details

There is a predict method for predicting class ids or memberships from the fitted clusterers.

Cobweb implements the Cobweb Fisher:1987 and Classit Gennari+Langley+Fisher:1989 clustering algorithms.

FarthestFirst provides the “farthest first traversal algorithm” by Hochbaum+Shmoys:1985, which works as a fast simple approximate clusterer modeled after simple \(k\)-means.

SimpleKMeans provides clustering with the \(k\)-means algorithm.

XMeans provides \(k\)-means extended by an “Improve-Structure part” and automatically determines the number of clusters.

DBScan provides the “density-based clustering algorithm” by Ester+Kriegel+Sander:1996. Note that noise points are assigned to NA.

References

Ester+Kriegel+Sander:1996, Fisher:1987, Gennari+Langley+Fisher:1989, Hochbaum+Shmoys:1985, Pelleg+Moore:2000, Witten+Frank:2005

Examples

cl1 <- SimpleKMeans(iris[, -5], Weka_control(N = 3))
cl1
table(predict(cl1), iris$Species)

if (FALSE) {
## Requires Weka package 'XMeans' to be installed.
## Use XMeans with a KDTree.
cl2 <- XMeans(iris[, -5],
              c("-L", 3, "-H", 7, "-use-kdtree",
                "-K", "weka.core.neighboursearch.KDTree -P"))
cl2
table(predict(cl2), iris$Species)
}