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From "Jeff Eastman (JIRA)" <>
Subject [jira] [Updated] (MAHOUT-1030) Regression: Clustered Points Should be WeightedPropertyVectorWritable not WeightedVectorWritable
Date Mon, 11 Jun 2012 15:38:44 GMT


Jeff Eastman updated MAHOUT-1030:

    Fix Version/s: 0.8

I'm moving this to 0.8 to allow for more discussion on whether or not it needs to be implemented
at all. Given that the existing pdf weights allow for distance to be computed I don't view
it as a showstopper for 0.7.

I committed the patch to ClusterDumperWriter after the release bits were cut but, since it
has not presented itself to date and since clustering no longer produces WeightedPropertyVectorWritables,
I don't view it as a showstopper for 0.7 either.
> Regression: Clustered Points Should be WeightedPropertyVectorWritable not WeightedVectorWritable
> ------------------------------------------------------------------------------------------------
>                 Key: MAHOUT-1030
>                 URL:
>             Project: Mahout
>          Issue Type: Bug
>          Components: Clustering, Integration
>    Affects Versions: 0.7
>            Reporter: Jeff Eastman
>            Assignee: Jeff Eastman
>             Fix For: 0.8
>         Attachments: MAHOUT-1030.patch
> Looks like this won't make it into this build. Pretty widespread impact on code and tests
and I don't know which properties were implemented in the old version. I will create a JIRA
and post my interim results.
> On 6/8/12 12:21 PM, Jeff Eastman wrote:
> > That's a reversion that evidently got in when the new ClusterClassificationDriver
was introduced. It should be a pretty easy fix and I will see if I can make the change before
Paritosh cuts the release bits tonight.
> >
> > On 6/7/12 1:00 PM, Pat Ferrel wrote:
> >> It appears that in kmeans the clusteredPoints are now written as WeightedVectorWritable
where in mahout 0.6 they were WeightedPropertyVectorWritable? This means that the distance
from the centroid is no longer stored here? Why? I hope I'm wrong because that is not a welcome
change. How is one to order clustered docs by distance from cluster centroid?
> >>
> >> I'm sure I could calculate the distance but that would mean looking up the centroid
for the cluster id given in the above WeightedVectorWritable, which means iterating through
all the clusters for each clustered doc. In my case the number of clusters could be fairly
> >>
> >> Am I missing something?
> >>
> >>
> >

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