[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.

MaxNumIterations [100]

  Maximum number of iterations allowed for the kmeans algorithm to converge.

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().