centres = kmeans(centres, data, options) [centres, options] = kmeans(centres, data, options) [centres, options, post, errlog] = kmeans(centres, data, options)
centres = kmeans(centres, data, options)
uses the batch K-means algorithm to set the centres of a cluster model.
The matrix data represents the data
which is being clustered, with each row corresponding to a vector.
The sum of squares error function is used. The point at which
a local minimum is achieved is returned as centres. The
error value at that point is returned in options(8).
[centres, options, post, errlog] = kmeans(centres, data, options)
also returns the cluster number (in a one-of-N encoding) for each data
point in post and a log of the error values after each cycle in
errlog.
The optional parameters have the following interpretations.
options(1) is set to 1 to display error values; also logs error
values in the return argument errlog.
If options(1) is set to 0,
then only warning messages are displayed. If options(1) is -1,
then nothing is displayed.
options(2) is a measure of the absolute precision required for the value
of centres at the solution. If the absolute difference between
the values of centres between two successive steps is less than
options(2), then this condition is satisfied.
options(3) is a measure of the precision required of the error
function at the solution. If the absolute difference between the
error functions between two successive steps is less than
options(3), then this condition is satisfied.
Both this and the previous condition must be
satisfied for termination.
options(14) is the maximum number of iterations; default 100.
kmeans can be used to initialise the centres of a Gaussian
mixture model that is then trained with the EM algorithm.
[priors, centres, var] = gmmunpak(p, md); centres = kmeans(centres, data, options); p = gmmpak(priors, centres, var); p = gmmem(p, md, data, options);
gmminit, gmmemCopyright (c) Ian T Nabney (1996-9)