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https://issues.apache.org/jira/browse/MAHOUT-843?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13143362#comment-13143362
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Jeff Eastman commented on MAHOUT-843:
-------------------------------------
This patch looks like a refinement of the earlier patch. Writing a Java driver
to orchestrate top-down clustering given the Config and Postprocessor instances
seems a useful experiment. What is needed to move this patch closer to trunk
is: 1) some unit tests of the Java classes, 2) a command line interface. This
last requirement is where I get back to my earlier question above: "how is this
better than using the existing [CLI] jobs [in a shell script]?"
To use the Java classes for top clusterer A and bottom clusterer B one needs to
provide all of the arguments for A and B. Given all the different flavors of A
and B which could be chosen, it still seems really complicated to define a
single CLI which can provide all the permutations. Do you have a strategy for
this?
I do think the postprocessor to split the clusteredPointsA into directories so
that multiple invocations of B can proceed is useful and I would suggest
focusing on that as a stand-alone CLI method first. This would be a minimal
first step and save the combinatoric explosion of A,B CLI arguments needed to
encapsulate the whole process. With some unit tests and an example script or
two, I could see that in trunk very soon.
> Top Down Clustering
> -------------------
>
> Key: MAHOUT-843
> URL: https://issues.apache.org/jira/browse/MAHOUT-843
> Project: Mahout
> Issue Type: New Feature
> Components: Clustering
> Affects Versions: 0.6
> Reporter: Paritosh Ranjan
> Labels: clustering, patch
> Fix For: 0.6
>
> Attachments: MAHOUT-843-patch, Top-Down-Clustering-patch
>
>
> Top Down Clustering works in multiple steps. The first step is to find
> comparative bigger clusters. The second step is to cluster the bigger chunks
> into meaningful clusters. This can performance while clustering big amount of
> data. And, it also removes the dependency of providing input clusters/numbers
> to the clustering algorithm.
> The "big" is a relative term, as well as the smaller "meaningful" terms. So,
> the control of this "bigger" and "smaller/meaningful" clusters will be
> controlled by the user.
> Which clustering algorithm to be used in the top level and which to use in
> the bottom level can also be selected by the user. Initially, it can be done
> for only one/few clustering algorithms, and later, option can be provided to
> use all the algorithms ( which suits the case ).
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