clusterkmeans {goCluster} | R Documentation

## Clusters a dataset with the kmeans function

### Description

This function is used in the goCluster framework to cluster a dataset with the kmeans function.

### Usage

    clusterkmeans(dataset, clusters, repeats)

### Arguments

`dataset` | The dataset to be clustered. This has to be a matrix.
`clusters` | This specifies the number of clusters that the dataset should be partitioned into.
`repeats` | It may be useful to repeat the clustering in order to get an impression of the variability of the clustering result. This option specifies the number of repeats.

### Details

Kmeans clustering will partition the dataset of the parent object into the number of clusters specified by the user. This will be repeated as often as specified in the repeats option. The class will return a list of groups that can subsequently be analyzed by statistical means for any enrichment of functional categories. The repetition of clustering is included since kmeans is no deterministic procedure. Depending on the initialization conditions the result may vary slightly. Thus repeating the process yields an average.

### Value

A "tree" (list of lists) of clusters. The first level will hold as many list elements as the number of times the clustering has been repeated. Each of these elements holds a number of lists equal to the number of clusters requested .Each of node on this second level hold the unique ids of the genes in the cluster.

### Author(s)

Gunnar Wrobel, work@gunnarwrobel.de, http://www.gunnarwrobel.de.

### See Also

`clusterAlgorithmClara-class` `clara`

### Examples

    require(cclust)

    ## Get the benomyl setup
    data(benomylsetup)

    ## Extract a fraction of the dataset
    benomyldata <- benomylsetup$data$dataset[1:200,]
    benomylids  <- benomylsetup$data$uniqueid[1:200]

    ## Cluster the dataset
    clusterkmeans(exprs(benomyldata), 4, 2)

[Package goCluster version 1.4.0 Index]