Provided by: mlpack-bin_4.5.0-1_amd64 
NAME
mlpack_kmeans - k-means clustering
SYNOPSIS
mlpack_kmeans -c int -i unknown [-a string] [-e bool] [-P bool] [-I unknown] [-E bool] [-K bool] [-l bool] [-m int] [-p double] [-r bool] [-S int] [-s int] [-V bool] [-C unknown] [-o unknown] [-h -v]
DESCRIPTION
This program performs K-Means clustering on the given dataset. It can return the learned
cluster assignments, and the centroids of the clusters. Empty clusters are not allowed by
default; when a cluster becomes empty, the point furthest from the centroid of the cluster
with maximum variance is taken to fill that cluster.
Optionally, the strategy to choose initial centroids can be specified. The k-means++
algorithm can be used to choose initial centroids with the ’--kmeans_plus_plus (-K)'
parameter. The Bradley and Fayyad approach ("Refining initial points for k-means
clustering", 1998) can be used to select initial points by specifying the '--refined_start
(-r)' parameter. This approach works by taking random samplings of the dataset; to specify
the number of samplings, the '--samplings (-S)' parameter is used, and to specify the
percentage of the dataset to be used in each sample, the '--percentage (-p)' parameter is
used (it should be a value between 0.0 and 1.0).
There are several options available for the algorithm used for each Lloyd iteration,
specified with the '--algorithm (-a)' option. The standard O(kN) approach can be used
('naive'). Other options include the Pelleg-Moore tree-based algorithm ('pelleg-moore'),
Elkan's triangle-inequality based algorithm ('elkan'), Hamerly's modification to Elkan's
algorithm ('hamerly'), the dual-tree k-means algorithm ('dualtree'), and the dual-tree k-
means algorithm using the cover tree ('dualtree-covertree').
The behavior for when an empty cluster is encountered can be modified with the
’--allow_empty_clusters (-e)' option. When this option is specified and there is a cluster
owning no points at the end of an iteration, that cluster's centroid will simply remain in
its position from the previous iteration. If the '--kill_empty_clusters (-E)' option is
specified, then when a cluster owns no points at the end of an iteration, the cluster
centroid is simply filled with DBL_MAX, killing it and effectively reducing k for the rest
of the computation. Note that the default option when neither empty cluster option is
specified can be time-consuming to calculate; therefore, specifying either of these
parameters will often accelerate runtime.
Initial clustering assignments may be specified using the ’--initial_centroids_file (-I)'
parameter, and the maximum number of iterations may be specified with the
'--max_iterations (-m)' parameter.
As an example, to use Hamerly's algorithm to perform k-means clustering with k=10 on the
dataset 'data.csv', saving the centroids to 'centroids.csv' and the assignments for each
point to 'assignments.csv', the following command could be used:
$ mlpack_kmeans --input_file data.csv --clusters 10 --output_file assignments.csv
--centroid_file centroids.csv
To run k-means on that same dataset with initial centroids specified in ’initial.csv' with
a maximum of 500 iterations, storing the output centroids in 'final.csv' the following
command may be used:
$ mlpack_kmeans --input_file data.csv --initial_centroids_file initial.csv --clusters 10
--max_iterations 500 --centroid_file final.csv
REQUIRED INPUT OPTIONS
--clusters (-c) [int]
Number of clusters to find (0 autodetects from initial centroids).
--input_file (-i) [unknown]
Input dataset to perform clustering on.
OPTIONAL INPUT OPTIONS
--algorithm (-a) [string]
Algorithm to use for the Lloyd iteration ('naive', 'pelleg-moore', 'elkan',
'hamerly', 'dualtree', or 'dualtree-covertree'). Default value 'naive'.
--allow_empty_clusters (-e) [bool]
Allow empty clusters to be persist.
--help (-h) [bool]
Default help info.
--in_place (-P) [bool]
If specified, a column containing the learned cluster assignments will be added to
the input dataset file. In this case, --output_file is overridden. (Do not use in
Python.)
--info [string]
Print help on a specific option. Default value ''.
--initial_centroids_file (-I) [unknown]
Start with the specified initial centroids.
--kill_empty_clusters (-E) [bool]
Remove empty clusters when they occur.
--kmeans_plus_plus (-K) [bool]
Use the k-means++ initialization strategy to choose initial points.
--labels_only (-l) [bool]
Only output labels into output file.
--max_iterations (-m) [int]
Maximum number of iterations before k-means terminates. Default value 1000.
--percentage (-p) [double]
Percentage of dataset to use for each refined start sampling (use when
--refined_start is specified). Default value 0.02.
--refined_start (-r) [bool]
Use the refined initial point strategy by Bradley and Fayyad to choose initial
points.
--samplings (-S) [int]
Number of samplings to perform for refined start
(use when --refined_start is specified).
Default value 100.
--seed (-s) [int]
Random seed. If 0, 'std::time(NULL)' is used. Default value 0.
--verbose (-v) [bool]
Display informational messages and the full list of parameters and timers at the
end of execution.
--version (-V) [bool]
Display the version of mlpack.
OPTIONAL OUTPUT OPTIONS
--centroid_file (-C) [unknown]
If specified, the centroids of each cluster will be written to the given file.
--output_file (-o) [unknown] Matrix to store output labels or labeled data to.
ADDITIONAL INFORMATION
For further information, including relevant papers, citations, and theory, consult the
documentation found at http://www.mlpack.org or included with your distribution of mlpack.