R: Prediction function for Mini-Batch-k-means

predict_MBatchKMeans {ClusterR} | R Documentation

## Prediction function for Mini-Batch-k-means

### Description

Prediction function for Mini-Batch-k-means

### Usage

    predict_MBatchKMeans(data, CENTROIDS, fuzzy = FALSE)

### Arguments

`data` | matrix or data frame
`CENTROIDS` | a matrix of initial cluster centroids. The rows of the CENTROIDS matrix should be equal to the number of clusters and the columns should equal the columns of the data.
`fuzzy` | either TRUE or FALSE. If TRUE then prediction probabilities will be calculated using the distance between observations and centroids.

### Details

This function takes the data and the output centroids and returns the clusters.

### Value

if fuzzy = TRUE the function returns a list with two attributes: a vector with the clusters and a matrix with cluster probabilities. Otherwise, it returns a vector with the clusters.

### Author(s)

Lampros Mouselimis

### Examples

    data(dietary_survey_IBS)

    dat = dietary_survey_IBS[, -ncol(dietary_survey_IBS)]

    dat = center_scale(dat)

    MbatchKm = MiniBatchKmeans(dat, clusters = 2, batch_size = 20, num_init = 5, early_stop_iter = 10)

    pr = predict_MBatchKMeans(dat, MbatchKm$centroids, fuzzy = FALSE)

[Package ClusterR version 1.2.2 Index]