Weka_clusterers {RWeka} | R Documentation |
R interfaces to Weka clustering algorithms.
Cobweb(x, control = NULL) FarthestFirst(x, control = NULL) SimpleKMeans(x, control = NULL)
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. |
There is a predict
method for
class prediction from the fitted clusterers.
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.
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). |
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.
I. H. Witten and Eibe Frank (2005). Data Mining: Practical Machine Learning Tools and Techniques. 2nd Edition, Morgan Kaufmann, San Francisco.
data(iris) cl <- SimpleKMeans(iris[, -5], c("-N", "3")) cl table(predict(cl), iris$Species)