Weka_clusterers {RWeka} | R Documentation

## 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).

### 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.

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.

### Examples

    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)

[Package RWeka version 0.4-1 Index]