📄 kmeans.htm
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<html><head><title>Netlab Reference Manual kmeans</title></head><body><H1> kmeans</H1><h2>Purpose</h2>Trains a k means cluster model.<p><h2>Synopsis</h2><PRE>centres = kmeans(centres, data, options)[centres, options] = kmeans(centres, data, options)[centres, options, post, errlog] = kmeans(centres, data, options)</PRE><p><h2>Description</h2><CODE>centres = kmeans(centres, data, options)</CODE>uses the batch K-means algorithm to set the centres of a cluster model.The matrix <CODE>data</CODE> represents the datawhich is being clustered, with each row corresponding to a vector.The sum of squares error function is used. The point at whicha local minimum is achieved is returned as <CODE>centres</CODE>. Theerror value at that point is returned in <CODE>options(8)</CODE>.<p><CODE>[centres, options, post, errlog] = kmeans(centres, data, options)</CODE>also returns the cluster number (in a one-of-N encoding) for each datapoint in <CODE>post</CODE> and a log of the error values after each cycle in<CODE>errlog</CODE>. The optional parameters have the following interpretations.<p><CODE>options(1)</CODE> is set to 1 to display error values; also logs error values in the return argument <CODE>errlog</CODE>.If <CODE>options(1)</CODE> is set to 0,then only warning messages are displayed. If <CODE>options(1)</CODE> is -1,then nothing is displayed.<p><CODE>options(2)</CODE> is a measure of the absolute precision required for the valueof <CODE>centres</CODE> at the solution. If the absolute difference betweenthe values of <CODE>centres</CODE> between two successive steps is less than<CODE>options(2)</CODE>, then this condition is satisfied.<p><CODE>options(3)</CODE> is a measure of the precision required of the errorfunction at the solution. If the absolute difference between theerror functions between two successive steps is less than<CODE>options(3)</CODE>, then this condition is satisfied.Both this and the previous condition must besatisfied for termination.<p><CODE>options(14)</CODE> is the maximum number of iterations; default 100.<p><h2>Example</h2><CODE>kmeans</CODE> can be used to initialise the centres of a Gaussian mixture model that is then trained with the EM algorithm.<PRE>[priors, centres, var] = gmmunpak(p, md);centres = kmeans(centres, data, options);p = gmmpak(priors, centres, var);p = gmmem(p, md, data, options);</PRE><p><h2>See Also</h2><CODE><a href="gmminit.htm">gmminit</a></CODE>, <CODE><a href="gmmem.htm">gmmem</a></CODE><hr><b>Pages:</b><a href="index.htm">Index</a><hr><p>Copyright (c) Ian T Nabney (1996-9)</body></html>⌨️ 快捷键说明
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