Cobweb(x, control = NULL)
FarthestFirst(x, control = NULL)
SimpleKMeans(x, control = NULL)
XMeans(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
predicting class ids or memberships from the fitted clusterers.
Cobweb
implements the Cobweb (Fisher, 1987) and Classit
(Gennari et al., 1989) clustering algorithms.
FarthestFirst
provides the SimpleKMeans
provides clustering with the $k$-means
algorithm.
XMeans
provides $k$-means extended by an
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 incremental concept formation. Artificial Intelligence, 40, 11--62. D. S. Hochbaum and D. B. Shmoys (1985). A best possible heuristic for the $k$-center problem, Mathematics of Operations Research, 10(2), 180--184.
D. Pelleg and A. W. Moore (2006). X-means: Extending K-means with Efficient Estimation of the Number of Clusters. In: Seventeenth International Conference on Machine Learning, 727--734. Morgan Kaufmann.
I. H. Witten and E. Frank (2005). Data Mining: Practical Machine Learning Tools and Techniques. 2nd Edition, Morgan Kaufmann, San Francisco.
cl1 <- SimpleKMeans(iris[, -5], Weka_control(N = 3))
cl1
table(predict(cl1), iris$Species)
## Use XMeans with a KDTree.
cl2 <- XMeans(iris[, -5],
c("-L", 3, "-H", 7, "-use-kdtree",
"-K", "weka.core.neighboursearch.KDTree -P"))
cl2
table(predict(cl2), iris$Species)
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