MLIclust {MLInterfaces} | R Documentation

## An interface to various unsupervised machine learning methods for ExpressionSets

### Description

This document describes a family of wrappers to R functions that perform clustering of multivariate datasets. For example, the `kmeansB` function is a wrapper for a call to `kmeans` for objects of class `ExpressionSet`. The interfaces of `MLInterfaces` provide a common calling sequence and common return value for machine learning code.

### Usage

    kmeansB(exprObj, k , iter.max, metric = "euclidean")

### Arguments

`exprObj` | An instance of the `exprset` class.
`k` | The number of clusters
`iter.max` | The maximum number of iterations to perform.
`metric` | See `knn` for a complete description.

### Details

See `kmeans` for a complete description, and for other functions named [f]B, see the documentation on `f`.

### Value

An object of class `clustOutput-class`.

### Author(s)

Jess Mar, VJ Carey <stvjc@channing.harvard.edu>

### See Also

`ldaB`

### Examples

    ##get these from the source files
    library(golubEsets)
    data(Golub_Merge)
    smallG <- Golub_Merge[1:60,]
    set.seed(1234) # needed for nnet initialization
    kmeansB( smallG, k=4 )
    agnesB( smallG, k=5, height=0, FALSE )
    dianaB( smallG, k=5, height=0, FALSE )
    pamB( smallG, k=5, height=0, FALSE )
    bclustB( smallG, k=5, height=0 )
    cmeansB( smallG, k=5, height=0 )
    cshellB( smallG, k=5, height=0 , iter.max=1)
    #lcaB( BsmG, k=3 )
    hclustB( smallG, k=4 )
    hclustB( smallG, height=40000 )

[Package MLInterfaces version 1.14.1 Index]