Mini-batch-k-means using RcppArmadillo

```
MiniBatchKmeans(
data,
clusters,
batch_size = 10,
num_init = 1,
max_iters = 100,
init_fraction = 1,
initializer = "kmeans++",
early_stop_iter = 10,
verbose = FALSE,
CENTROIDS = NULL,
tol = 1e-04,
tol_optimal_init = 0.3,
seed = 1
)
```

data

matrix or data frame

clusters

the number of clusters

batch_size

the size of the mini batches

num_init

number of times the algorithm will be run with different centroid seeds

max_iters

the maximum number of clustering iterations

init_fraction

percentage of data to use for the initialization centroids (applies if initializer is *kmeans++* or *optimal_init*). Should be a float number between 0.0 and 1.0.

initializer

the method of initialization. One of, *optimal_init*, *quantile_init*, *kmeans++* and *random*. See details for more information

early_stop_iter

continue that many iterations after calculation of the best within-cluster-sum-of-squared-error

verbose

either TRUE or FALSE, indicating whether progress is printed during clustering

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 be equal to the columns of the data

tol

a float number. If, in case of an iteration (iteration > 1 and iteration < max_iters) 'tol' is greater than the squared norm of the centroids, then kmeans has converged

tol_optimal_init

tolerance value for the 'optimal_init' initializer. The higher this value is, the far appart from each other the centroids are.

seed

integer value for random number generator (RNG)

a list with the following attributes: centroids, WCSS_per_cluster, best_initialization, iters_per_initialization

This function performs k-means clustering using mini batches.

---------------initializers----------------------

**optimal_init** : this initializer adds rows of the data incrementally, while checking that they do not already exist in the centroid-matrix [ experimental ]

**quantile_init** : initialization of centroids by using the cummulative distance between observations and by removing potential duplicates [ experimental ]

**kmeans++** : kmeans++ initialization. Reference : http://theory.stanford.edu/~sergei/papers/kMeansPP-soda.pdf AND http://stackoverflow.com/questions/5466323/how-exactly-does-k-means-work

**random** : random selection of data rows as initial centroids

http://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf, https://github.com/siddharth-agrawal/Mini-Batch-K-Means

# NOT RUN { 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) # }