RWeka (version 0.2-1)

Weka_clusterers: R/Weka Clusterers

Description

R interfaces to Weka clustering algorithms.

Usage

Cobweb(x, control = NULL)
FarthestFirst(x, control = NULL)
SimpleKMeans(x, control = NULL)
XMeans(x, control = NULL)
DBScan(x, control = NULL)

Arguments

x

an R object with the data to be clustered.

control

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

Value

• 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).

Details

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 provides the farthest first traversal algorithm by Hochbaum and Shmoys, which works as a fast simple approximate clusterer modelled after simple $k$-means.

SimpleKMeans provides clustering with the k-means algorithm. XMeans provides k-means extended by an Improve-Structure part and automatically determines the number of clusters.

DBScan provides the density-based clustering algorithm by Ester, Kriegel, Sander, and Xu. Note that noise points are assigned to NA.

References

Ester M., Kriegel H.-P., Sander J., Xu X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise, Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining (KDD'96), Portland, OR, 226--231.

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. Hochbaum and Shmoys (1985). A best possible heuristic for the $k$-center problem, Mathematics of Operations Research, 10(2), 180--184.

Dan Pelleg and Andrew 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.

I. H. Witten and Eibe Frank (2005). Data Mining: Practical Machine Learning Tools and Techniques. 2nd Edition, Morgan Kaufmann, San Francisco.

Examples

data("iris")cl1 <- SimpleKMeans(iris[, -5], Weka_control(N = 3))
cl1
table(predict(cl1), iris$Species)

cl2 <- XMeans(iris[, -5], c("-L", 3, "-H", 7, "weka.core.KDTree", "-P"))
cl2
table(predict(cl2), iris$Species)