Mini-batch-k-means using RcppArmadillo
Usage
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
)Arguments
- 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)
Value
a list with the following attributes: centroids, WCSS_per_cluster, best_initialization, iters_per_initialization
Details
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
References
http://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf, https://github.com/siddharth-agrawal/Mini-Batch-K-Means
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)