R interfaces to Weka clustering algorithms.
Cobweb(x, control = NULL) FarthestFirst(x, control = NULL) SimpleKMeans(x, control = NULL) XMeans(x, control = NULL) DBScan(x, control = NULL)
- an R object with the data to be clustered.
- an object of class
Weka_control, or a character vector of control options, or
NULL(default). Available options can be obtained on-line using the Weka Option Wizard
There is a
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
XMeans provides $k$-means extended by an
DBScan provides 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).
XMeans requires Weka package
DBScan requires Weka package
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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$Species) ## Requires Weka package 'XMeans' to be installed. ## Use XMeans with a KDTree. cl2 <- XMeans(iris[, -5], c("-L", 3, "-H", 7, "-use-kdtree", "-K", "weka.core.neighboursearch.KDTree -P")) cl2 table(predict(cl2), iris$Species)