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https://issues.apache.org/jira/browse/MAHOUT-843?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13148248#comment-13148248
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Paritosh Ranjan edited comment on MAHOUT-843 at 11/11/11 4:10 AM:
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I have taken all incoming changes and created the patch. Also added
TopDownClusteringPathConstants. Can't see any other external reference.
The clusterDataMR and clusterDataSeq, both overwrite the points in
clusteredPoints when the input file provided has more than one paths, which is
the case in the input of bottom level cluster.
The test case does top level clustering, asserts cluster output processor, both
of which works fine. Then it is asserting bottom level clustering which shows
the problem. Only one point is written(overridden) in one cluster. This can be
seen while debugging clusterDataSeq.
was (Author: paritoshranjan):
I have taken all incoming changes and created the patch. Also added
TopDownClusteringPathConstants. Can't see any other external reference.
The clusterDataMR and clusterDataSeq, both overwrite the clusteredPoints when
the input file provided has more than one paths, which is the case in the input
of bottom level cluster.
The test case does top level clustering, asserts cluster output processor, both
of which works fine. Then it is asserting bottom level clustering which shows
the problem. Only one point is written(overridden) in one cluster. This can be
seen while debugging clusterDataSeq.
> 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, MAHOUT-843-patch-only-postprocessor,
> MAHOUT-843-patch-only-postprocessor-v1, MAHOUT-843-patch-v1,
> 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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