Sorry for replying via the dev list, but I am without Internet access beyond my 
phone. Yes, unless anyone testing can find issues with this patch (or with the 
one Grant posted earlier, as mine contains his), it is meant to be committed. 

Due to the aforementioned lack of Internet, if someone could commit this for me 
that's be fantastic.

On Dec 28, 2011, at 17:13, "Jeff Eastman (Commented) (JIRA)" <j...@apache.org> 
wrote:

> 
>    [ 
> https://issues.apache.org/jira/browse/MAHOUT-524?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13176876#comment-13176876
>  ] 
> 
> Jeff Eastman commented on MAHOUT-524:
> -------------------------------------
> 
> Shannon, is this patch ready to commit? I've installed it and verified that 
> DisplaySpectralKMeans is indeed finding clusters. By increasing the 
> numClusters from 2 to 3 it now does a credible job of finding the 3 clusters 
> present in the generated data.
> 
>> DisplaySpectralKMeans example fails
>> -----------------------------------
>> 
>>                Key: MAHOUT-524
>>                URL: https://issues.apache.org/jira/browse/MAHOUT-524
>>            Project: Mahout
>>         Issue Type: Bug
>>         Components: Clustering
>>   Affects Versions: 0.4, 0.5
>>           Reporter: Jeff Eastman
>>           Assignee: Shannon Quinn
>>             Labels: clustering, k-means, visualization
>>            Fix For: 0.6
>> 
>>        Attachments: EclipseLog_20110918.txt, MAHOUT-524.patch, 
>> MAHOUT-524.patch, MAHOUT-524.patch, SpectralKMeans_fail_20110919.txt, 
>> aff.txt, raw.txt, screenshot-1.jpg, spectralkmeans.png
>> 
>> 
>> I've committed a new display example that attempts to push the standard 
>> mixture of models data set through spectral k-means. After some tweaking of 
>> configuration arguments and a bug fix in EigenCleanupJob it runs spectral 
>> k-means to completion. The display example is expecting 2-d clustered points 
>> and the example is producing 5-d points. Additional I/O work is needed 
>> before this will play with the rest of the clustering algorithms. 
> 
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