clusterpam {goCluster} | R Documentation

## Clusters a dataset with the pam function

### Description

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

### Usage

    clusterpam(dataset, clusters, distance = "euclidian")

### 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.
`distance` | The distance metric that is going to be used by pam.

### Details

PAM clustering will partition the dataset of the parent object into the number of clusters specified by the user. This list of groups can subsequently be analyzed by statistical means for any enrichment of functional categories. PAM is considered to be more stable than K-means clustering and offers better techniques for validating the results e.g. the stability of clusters. It yields a deterministic outcome, but might take very long on large datasets (see `clara`).

### 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.

### Examples

    require(cluster)

    ## 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
    clusterpam(exprs(benomyldata), 4)

[Package goCluster version 1.6.0 Index]