Weka_clusterers: R/Weka Clusterers

In RWeka: R/Weka Interface

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 WOW, or the Weka documentation.

Details

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 “farthest first traversal algorithm” by Hochbaum and Shmoys, which works as a fast simple approximate clusterer modeled 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.

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).

Note

XMeans requires Weka package XMeans to be installed.

DBScan requires Weka package optics_dbScan to be installed.

References

M. Ester, H.-P. Kriegel, J. Sander, and X. Xu (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD'96), Portland, OR, 226–231. AAAI Press.

D. H. Fisher (1987). Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2/2, 139–172. doi: 10.1023/A:1022852608280.

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. doi: 10.1287/moor.10.2.180.

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.

Examples

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

## Not run: 
## 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)

## End(Not run)

RWeka documentation built on March 7, 2023, 6:21 p.m.