Daniel@0: Daniel@0: Daniel@0: Daniel@0: Netlab Reference Manual kmeans Daniel@0: Daniel@0: Daniel@0: Daniel@0:

kmeans Daniel@0:

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Daniel@0: Purpose Daniel@0:

Daniel@0: Trains a k means cluster model. Daniel@0: Daniel@0:

Daniel@0: Synopsis Daniel@0:

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Daniel@0: centres = kmeans(centres, data, options)
Daniel@0: [centres, options] = kmeans(centres, data, options)
Daniel@0: [centres, options, post, errlog] = kmeans(centres, data, options)
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Daniel@0: Description Daniel@0:

Daniel@0: Daniel@0: centres = kmeans(centres, data, options) Daniel@0: uses the batch K-means algorithm to set the centres of a cluster model. Daniel@0: The matrix data represents the data Daniel@0: which is being clustered, with each row corresponding to a vector. Daniel@0: The sum of squares error function is used. The point at which Daniel@0: a local minimum is achieved is returned as centres. The Daniel@0: error value at that point is returned in options(8). Daniel@0: Daniel@0:

[centres, options, post, errlog] = kmeans(centres, data, options) Daniel@0: also returns the cluster number (in a one-of-N encoding) for each data Daniel@0: point in post and a log of the error values after each cycle in Daniel@0: errlog. Daniel@0: Daniel@0: The optional parameters have the following interpretations. Daniel@0: Daniel@0:

options(1) is set to 1 to display error values; also logs error Daniel@0: values in the return argument errlog. Daniel@0: If options(1) is set to 0, Daniel@0: then only warning messages are displayed. If options(1) is -1, Daniel@0: then nothing is displayed. Daniel@0: Daniel@0:

options(2) is a measure of the absolute precision required for the value Daniel@0: of centres at the solution. If the absolute difference between Daniel@0: the values of centres between two successive steps is less than Daniel@0: options(2), then this condition is satisfied. Daniel@0: Daniel@0:

options(3) is a measure of the precision required of the error Daniel@0: function at the solution. If the absolute difference between the Daniel@0: error functions between two successive steps is less than Daniel@0: options(3), then this condition is satisfied. Daniel@0: Both this and the previous condition must be Daniel@0: satisfied for termination. Daniel@0: Daniel@0:

options(14) is the maximum number of iterations; default 100. Daniel@0: Daniel@0:

Daniel@0: Example Daniel@0:

Daniel@0: kmeans can be used to initialise the centres of a Gaussian Daniel@0: mixture model that is then trained with the EM algorithm. Daniel@0:

Daniel@0: 
Daniel@0: [priors, centres, var] = gmmunpak(p, md);
Daniel@0: centres = kmeans(centres, data, options);
Daniel@0: p = gmmpak(priors, centres, var);
Daniel@0: p = gmmem(p, md, data, options);
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Daniel@0: See Also Daniel@0:

Daniel@0: gmminit, gmmem

Daniel@0: Pages: Daniel@0: Index Daniel@0:

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