Weka_clusterers

0th

Percentile

R/Weka Clusterers

R interfaces to Weka clustering algorithms.

Keywords
cluster
Usage
Cobweb(x, control = NULL)
FarthestFirst(x, control = NULL)
SimpleKMeans(x, control = NULL)
Arguments
x
an R object with the data to be clustered.
control
a character vector with control options, or NULL (default). Available options can be obtained on-line using the Weka Option Wizard WOW, or the Weka documentation.
Details

Cobweb implements the Cobweb (Fisher, 1987) and Classit (Gennari et al., 1989) clustering algorithms. FarthestFirst implements the farthest first traversal algorithm by Hochbaum and Shmoys, which works as a fast simple approximate clusterer modelled after simple $k$-means.

Value

  • A list inheriting from class Weka_clusterers with components including
  • clusterera 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_idsa 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).

References

D. H. Fisher (1987). Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2/2, 139--172.

J. Gennari, P. Langley and D. H. Fisher (1989). Models of incremenal concept formation. Artificial Intelligence, 40, 11--62. Hochbaum and Shmoys (1985). A best possible heuristic for the $k$-center problem, Mathematics of Operations Research, 10(2), 180--184.

Aliases
  • Cobweb
  • FarthestFirst
  • SimpleKMeans
Examples
data(iris)
cl <- SimpleKMeans(iris[, -5], c("-N", "3"))
cl
table(cl$class_ids, iris$Species)
Documentation reproduced from package RWeka, version 0.1-0, License: GPL version 2 or newer

Community examples

Looks like there are no examples yet.