R interfaces to Weka clustering algorithms.
Cobweb(x, control = NULL) FarthestFirst(x, control = NULL) SimpleKMeans(x, control = NULL)
- an R object with the data to be clustered.
- 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.
Cobweb implements the Cobweb (Fisher, 1987) and Classit
(Gennari et al., 1989) clustering algorithms.
FarthestFirst implements the
- A list inheriting from class
Weka_clustererswith components including
clusterer a reference (of class
jobjRef) to a Java object obtained by applying the Weka
buildClusterermethod 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
clusterInstancemethod 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.
data(iris) cl <- SimpleKMeans(iris[, -5], c("-N", "3")) cl table(cl$class_ids, iris$Species)