Prediction function for Mini-Batch-k-means
Usage
predict_MBatchKMeans(data, CENTROIDS, fuzzy = FALSE, updated_output = FALSE)
# S3 method for class 'MBatchKMeans'
predict(object, newdata, 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.
- updated_output
either TRUE or FALSE. If TRUE then the 'predict_MBatchKMeans' function will follow the same output object behaviour as the 'predict_KMeans' function (if fuzzy is TRUE it will return probabilities otherwise it will return the hard clusters). This parameter will be removed in version 1.4.0 because this will become the default output format.
- object, newdata, ...
arguments for the `predict` generic
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.
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)
#> Warning: `predict_MBatchKMeans()` was deprecated in ClusterR 1.3.0.
#> ℹ Beginning from version 1.4.0, if the fuzzy parameter is TRUE the function
#> 'predict_MBatchKMeans' will return only the probabilities, whereas currently
#> it also returns the hard clusters