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https://issues.apache.org/jira/browse/MAHOUT-30?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12659273#action_12659273
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Ted Dunning commented on MAHOUT-30:
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Some references include a relatively dense article by McCullagh and Yang:
http://ba.stat.cmu.edu/journal/2008/vol03/issue01/yang.pdf
There is also a more approachable example in Chris Bishop's book on Machine
Learning. See
http://research.microsoft.com/en-us/um/people/cmbishop/PRML/index.htm. I think
that chapter 9 is where the example of clustering using a mixture model is
found.
The Neal and Blei references from the McCullagh and Yang paper are also good.
Zoubin Gharamani has some very nice tutorials out which describe why
non-parametric Bayesian approaches to problems are very cool. One is at
http://learning.eng.cam.ac.uk/zoubin/talks/uai05tutorial-b.pdf but here are
video versions about as well.
> dirichlet process implementation
> --------------------------------
>
> Key: MAHOUT-30
> URL: https://issues.apache.org/jira/browse/MAHOUT-30
> Project: Mahout
> Issue Type: New Feature
> Components: Clustering
> Reporter: Isabel Drost
> Assignee: Jeff Eastman
> Attachments: jeastman.vcf, MAHOUT-30.patch, MAHOUT-30b.patch,
> MAHOUT-30c.patch
>
>
> Copied over from original issue:
> > Further extension can also be made by assuming an infinite mixture model.
> > The implementation is only slightly more difficult and the result is a
> > (nearly)
> > non-parametric clustering algorithm.
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