MbkmeansParam-class {mbkmeans} | R Documentation

## Mini-batch k-means clustering

### Description

Run the mini-batch k-means `mbkmeans` function with the specified number of centers within `clusterRows` from the `bluster` Bioconductor package.

### Usage

    MbkmeansParam(centers, ...)

    ## S4 method for signature 'ANY,MbkmeansParam'
    clusterRows(x, BLUSPARAM, full = FALSE)

### Arguments

`centers` | An integer scalar specifying the number of centers. Alternatively, a function that takes the number of observations and returns the number of centers. Note, the `mbkmeans` function uses the argument `clusters` argument to represent this argument. However, we use `centers` to match
`...` | Further arguments to pass to `mbkmeans`.
`x` | A numeric matrix-like object where rows represent observations and columns represent variables.
`BLUSPARAM` | A MbkmeansParam object.
`full` | Logical scalar indicating whether the full mini-batch k-means statistics should be returned.

### Details

The`clusterRows` function from the `bluster` Bioconductor package is a generic that controls dispatch to different clustering algorithms. Here we add the option to use the `mbkmeans` clustering algorithm with `clusterRows`. This class usually requires the user to specify the number of clusters beforehand. However, we can also allow the number of clusters to vary as a function of the number of observations. The latter is occasionally useful, e.g., to allow the clustering to automatically become more granular for large datasets.

To modify an existing MbkmeansParam object `x`, users can simply call `x[[i]]` or `x[[i]] <- value` where `i` is any argument used in the constructor.

### Value

The `MbkmeansParam` constructor will return a MbkmeansParam object with the specified parameters.

The `clusterRows` method will return a factor of length equal to `nrow(x)` containing the cluster assignments. If `full=TRUE`, a list is returned with `clusters` (the factor, as above) and `objects`; the latter will contain the direct output of `mbkmeans`.

### Author(s)

Stephanie Hicks

### See Also

`mbkmeans`, which actually does all the heavy lifting.

### Examples

    clusterRows(iris[,1:4], MbkmeansParam(centers=3))
    clusterRows(iris[,1:4], MbkmeansParam(centers=3, batch_size=10))
    clusterRows(iris[,1:4], MbkmeansParam(centers=3, batch_size=10,
                compute_labels=TRUE, calc_wcss=TRUE))

[Package mbkmeans version 1.6.1 Index]