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https://issues.apache.org/jira/browse/MAHOUT-843?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Paritosh Ranjan updated MAHOUT-843:
-----------------------------------

    Attachment: MAHOUT-843-patch-v1

Hi Jeff, I have added the patch which has the CLI and Junit test for 
PathDirectory and ClusterOutputPostProcessor. These two classes combine the 
post processor, along with TopDownClusteringPathConstants.

I have tried to mirror a test case from TestKMeansClustering and running the 
post processor on it. My dev env is having trouble running Mahout test cases ( 
its windows, though I am having cygwin, still it creates problem ). So, I would 
also appreciate your help in adding some meaningful assertions in test cases. 

This is also my first attempt at creating the CLI, so please help if I did 
something wrong there. 

Thanks.
                
> 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-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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