wolffd@0: wolffd@0: wolffd@0: wolffd@0: Netlab Reference Manual kmeans wolffd@0: wolffd@0: wolffd@0: wolffd@0:

kmeans wolffd@0:

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wolffd@0: Purpose wolffd@0:

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

wolffd@0: Synopsis wolffd@0:

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

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

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

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

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

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

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

wolffd@0: Example wolffd@0:

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

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

wolffd@0: gmminit, gmmem

wolffd@0: Pages: wolffd@0: Index wolffd@0:

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