NAME¶

SYNOPSIS¶

 mlpack_kmeans [-h] [-v]  

DESCRIPTION¶

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').

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 -e or -E will often accelerate runtime.

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 INPUT OPTIONS¶

--clusters (-c) [int]

Number of clusters to find (0 autodetects from initial centroids).

--input_file (-i) [string]

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)

Allow empty clusters to be persist.

--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 ''.

--kill_empty_clusters (-E)

Remove empty clusters when they occur.

--labels_only (-l)

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)

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.

OPTIONAL OUTPUT OPTIONS¶

--output_file (-o) [string]

File to write output labels or labeled data to. Default value ''.

ADDITIONAL INFORMATION¶

ADDITIONAL INFORMATION¶