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List:       wekalist
Subject:    Re: [Wekalist] Fitting Multivariate Gaussian Mixture Models
From:       Mark Hall <mhall () pentaho ! com>
Date:       2012-06-26 7:23:55
Message-ID: 4FE9638B.9010806 () pentaho ! com
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On 21/06/12 7:47 AM, Becksfort, Jared wrote:
> Hello,
>
> I am trying to use Weka to fit a multivariate Gaussian mixture model.  I
> want to give it an arff file and have it return the means of the
> components and a covariance matrix for each component.  I have tried
> using the Simple EM clusterer, but that only returns a set of means and
> standard deviations.  It also seems to try to cluster (hence the name)
> data rows into components, which doesn’t make sense in my case.  I have
> tried to use the EMImputation filter to preprocess, but it only runs for
> a couple of seconds and I can’t tell if it is actually doing anything.
> Either way, it doesn’t seem to be returning model parameters.
>
> I have had some luck with mixtools in R, but it is quite slow and I
> would like to integrate the process into my Java program without
> requiring users to download R.
>
> For an input file, it would look something like this (but with a lot
> more rows):
>
> @relation tissue_pixels
>
> %MRI channels
>
> @attribute t1 numeric
>
> @attribute t1ce numeric
>
> @attribute t2 numeric
>
> @attribute flair numeric
>
> % pixel values for each MRI channel
>
> @DATA
>
> 128,121,171,114
>
> 142,125,163,121
>
> 144,119,148,147
>
> 136,88,167,124
>
> 144,116,158,136
>
> 130,125,153,129
>
> 128,110,152,132
>
> 127,112,158,143
>
> Thanks!
>
> Jared Becksfort

I'm afraid that Weka doesn't have a multivariate mixture model. The EM 
clusterer in Weka treats attributes as independent of one another given 
the component/cluster, much like naive Bayes for classification.

Cheers,
Mark.



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