Provided by: mlpack-bin_2.0.1-1_amd64 
NAME
mlpack_kmeans - k-means clustering
SYNOPSIS
mlpack_kmeans [-h] [-v] -c int -i string [-a string] [-e] [-C string] [-P] [-I string] [-l] [-m int] [-o string] [-p double] [-r] [-S int] [-s int] -V
DESCRIPTION
This program performs K-Means clustering on the given dataset, storing the learned cluster assignments
either as a column of labels in the file containing the input dataset or in a separate file. 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 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) option. This approach works by
taking random samples of the dataset; to specify the number of samples, the --samples parameter is used,
and to specify the percentage of the dataset to be used in each sample, the --percentage 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').
As of October 2014, the --overclustering option has been removed. If you want this support back, let us
know -- file a bug at https://github.com/mlpack/mlpack/ or get in touch through another means.
REQUIRED OPTIONS
--clusters (-c) [int]
Number of clusters to find (0 autodetects from initial centroids).
--input_file (-i) [string]
Input dataset to perform clustering on.
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)
Allow empty clusters to be created.
--centroid_file (-C) [string]
If specified, the centroids of each cluster will be written to the given file. Default value ''.
--help (-h)
Default help info.
--in_place (-P)
If specified, a column containing the learned cluster assignments will be added to the input
dataset file. In this case, --outputFile is overridden.
--info [string]
Get help on a specific module or option. Default value ''.
--initial_centroids (-I) [string]
Start with the specified initial centroids. Default value ''.
--labels_only (-l)
Only output labels into output file.
--max_iterations (-m) [int]
Maximum number of iterations before K-Means terminates. Default value 1000.
--output_file (-o) [string]
File to write output labels or labeled data to. Default value ''.
--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)
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)
Display informational messages and the full list of parameters and timers at the end of execution.
--version (-V)
Display the version of mlpack.
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.
mlpack_kmeans(1)