buildKmknn {BiocNeighbors} | R Documentation

## Pre-cluster points with k-means

### Description

Perform k-means clustering in preparation for a KMKNN nearest-neighbors search.

### Usage

    buildKmknn(X, transposed=FALSE, distance=c("Euclidean", "Manhattan"), ...)

### Arguments

`X` | A numeric matrix where rows correspond to data points and columns correspond to variables (i.e., dimensions).
`transposed` | Logical scalar indicating whether `X` is transposed, i.e., rows are variables and columns are data points.
`distance` | String specifying the type of distance to use.
`...` | Further arguments to pass to `kmeans`.

### Details

This function is automatically called by `findKmknn` and related functions. However, it can be called directly by the user to save time if multiple queries are to be performed to the same `X`.

### Value

A KmknnIndex object containing:

- `data`, a numeric matrix with points in the columns and dimensions in the rows, i.e., transposed relative to the input. Points have also been reordered to improve data locality during the nearest-neighbor search. Specifically, points in the same cluster are contiguous and ordered by increasing distance from the cluster center.

- `clusters`, itself a list containing:

  - `centers`, a numeric matrix of cluster center coordinates where each column corresponds to a cluster.

  - `info`, another list of length equal to the number of clusters. Each entry corresponds to a column of `centers` (let's say cluster j) and is a list of length 2. The first element is the zero-index of the first cell in the output `X` that is assigned to j. The second element is the distance of each point in the cluster from the cluster center.

- `order`, an integer vector specifying how rows in `X` have been reordered in columns of `data`.

- `NAMES`, a character vector or `NULL` equal to `rownames(X)`.

- `distance`, a string specifying the distance metric used.

### Author(s)

Aaron Lun

### See Also

See `kmeans` for optional arguments.

See `KmknnIndex` for details on the output class.

See `findKmknn`, `queryKmknn` and `findNeighbors` for dependent functions.

### Examples

    Y <- matrix(rnorm(100000), ncol=20)
    out <- buildKmknn(Y)
    out

[Package BiocNeighbors version 1.4.2 Index]