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https://issues.apache.org/jira/browse/MAHOUT-479?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13020336#comment-13020336
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Jeff Eastman commented on MAHOUT-479:
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Ted: Yeah... this is what I had in mind when I said grand unified theory.
On Wed, Apr 13, 2011 at 9:24 PM, Jeff Eastman <[email protected]>wrote:
> This could potentially collapse kmeans, fuzzyk and Dirichlet into a single
> implementation too:
>
> - Begin with a prior ClusterClassifier containing the appropriate sort of
> Cluster, in clusters-n
> - For each input Vector, compute the pdf vector using CC.classify()
> -- For kmeans, train the most likely model from the pdf vector
> -- For Dirichlet, train the model selected by the multinomial of the pfd
> vector * mixture vector
> -- For fuzzyk, train each model by its normalized pdf (would need a
> new classify method for this)
> - Close the CC, computing all posterior model parameters
> - Serialize the CC into clusters-n+1
>
> Now that would really be cool
>
>
> On 4/13/11 9:00 PM, Jeff Eastman wrote:
>
>> Here's how I got there:
>>
>> - ClusterClassifier holds a "List<Cluster> models;" field as its only
>> state just like VectorModelClassifier does
>> - Started with ModelSerializerTest since you suggested being
>> compatible with ModelSerializer
>> - This tests OnlineLogisticRegression, CrossFoldLearner and
>> AdaptiveLogisticRegression
>> - The first two are also subclasses of AbstractVectorClassifier just
>> like ClusterClassifier
>> - The tests pass OLR and CFL learners to train(OnlineLearner) so it
>> made sense for a CC to be an OL too
>> - The new CC.train(...) methods map to "models.get(actual).observe()"
>> in
>> Cluster.observe(V)
>> - CC.close() maps to cluster.computeParameters() for each model which
>> computes the posterior cluster parameters
>> - Now the CC is ready for another iteration or to classify, etc.
>>
>> So, the cluster iteration process starts with a prior List<Cluster>
>> which is used to construct the ClusterClassifier. Then in each
>> iteration each point is passed to CC.classify() and the maximum
>> probability element index in the returned Vector is used to train()
>> the CC. Since all the DistanceMeasureClusters contain their
>> appropriate DistanceMeasure, the one with the maximum pdf() is the
>> closest. Just what kmeans already does but done less efficiently (it
>> uses just the minimum distance, but pdf() = e^-distance so the closest
>> cluster has the largest pdf()).
>>
>> Finally, instead of passing in a List<Cluster> in the KMeansClusterer
>> I can just carry around a CC which wraps it. Instead of serializing a
>> List<Cluster> at the end of each iteration I can just serialize the
>> CC. At the beginning of the next iteration, I just deserialize it and go.
> Streamline classification/ clustering data structures
> -----------------------------------------------------
>
> Key: MAHOUT-479
> URL: https://issues.apache.org/jira/browse/MAHOUT-479
> Project: Mahout
> Issue Type: Improvement
> Components: Classification, Clustering
> Affects Versions: 0.1, 0.2, 0.3, 0.4
> Reporter: Isabel Drost
> Assignee: Isabel Drost
>
> Opening this JIRA issue to collect ideas on how to streamline our
> classification and clustering algorithms to make integration for users easier
> as per mailing list thread http://markmail.org/message/pnzvrqpv5226twfs
> {quote}
> Jake and Robin and I were talking the other evening and a common lament was
> that our classification (and clustering) stuff was all over the map in terms
> of data structures. Driving that to rest and getting those comments even
> vaguely as plug and play as our much more advanced recommendation components
> would be very, very helpful.
> {quote}
> This issue probably also realates to MAHOUT-287 (intention there is to make
> naive bayes run on vectors as input).
> Ted, Jake, Robin: Would be great if someone of you could add a comment on
> some of the issues you discussed "the other evening" and (if applicable) any
> minor or major changes you think could help solve this issue.
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