Cobweb(x, control = NULL)
FarthestFirst(x, control = NULL)
SimpleKMeans(x, control = NULL)
DBScan(x, control = NULL)
Weka_control
, or a
character vector of control options, or NULL
(default).
Available options can be obtained on-line using the Weka Option
Wizard
Weka_clusterers
with components
includingjobjRef
) to a Java object
obtained by applying the Weka buildClusterer
method to the
training instances using the given control options.clusterInstance
method for the built clusterer and each
instance).predict
method for
class prediction from the fitted clusterers.
Cobweb
implements the Cobweb (Fisher, 1987) and Classit
(Gennari et al., 1989) clustering algorithms.
FarthestFirst
provides the DBScan
provides the NA
.
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], Weka_control(N = 3))
cl
table(predict(cl), iris$Species)
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