/usr/share/octave/site/m/vlfeat/toolbox/kmeans/vl_kmeans.m is in octave-vlfeat 0.9.17+dfsg0-6build1.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | %VL_KMEANS Cluster data using k-means
% [C, A] = VL_KMEANS(X, NUMCENTERS) clusters the columns of the
% matrix X in NUMCENTERS centers C using k-means. X may be either
% SINGLE or DOUBLE. C has the same number of rows of X and NUMCENTER
% columns, with one column per center. A is a UINT32 row vector
% specifying the assignments of the data X to the NUMCENTER
% centers.
%
% [C, A, ENERGY] = VL_KMEANS(...) returns the energy of the solution
% (or an upper bound for the ELKAN algorithm) as well.
%
% KMEANS() supports different initialization and optimization
% methods and different clustering distances. Specifically, the
% following options are supported:
%
% Verbose::
% Increase the verbosity level (may be specified multiple times).
%
% Distance:: [L2]
% Use either L1 or L2 distance.
%
% Initialization::
% Use either random data points (RANDSEL) or k-means++ (PLUSPLUS)
% to initialize the centers.
%
% Algorithm:: [LLOYD]
% One of LLOYD, ELKAN, or ANN. LLOYD is the standard Lloyd
% algorithm (similar to expectation maximisation). ELKAN is a
% faster version of LLOYD using triangular inequalities to cut
% down significantly the number of sample-to-center
% comparisons. ANN is the same as Lloyd, but uses an approximated
% nearest neighbours (ANN) algorithm to accelerate the
% sample-to-center comparisons. The latter is particularly
% suitable for very large problems.
%
% NumRepetitions:: [1]
% Number of time to restart k-means. The solution with minimal
% energy is returned.
%
% The following options tune the KD-Tree forest used for ANN
% computations in the ANN algorithm (see also VL_KDTREEBUILD()
% andVL_KDTREEQUERY()).
%
% NumTrees:: [3]
% The number of trees int the randomized KD-Tree forest.
%
% MaxNumComparisons:: [100]
% Maximum number of sample-to-center comparisons when searching
% for the closest center.
%
% Example::
% VL_KMEANS(X, 10, 'verbose', 'distance', 'l1', 'algorithm',
% 'elkan') clusters the data point X using 10 centers, l1
% distance, and the Elkan's algorithm.
%
% See also: VL_HELP().
% Authors: Andrea Vedaldi
% Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
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