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https://issues.apache.org/jira/browse/MAHOUT-524?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13095021#comment-13095021
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Lance Norskog edited comment on MAHOUT-524 at 9/1/11 12:39 AM:
---------------------------------------------------------------

Running DisplaySpectralKMeans gives this error:

FileNotFound:Exception
examples/output/calculations/laplacian-48/tmp/data not found

In fact, the data is stored here:

examples/output/calculations/laplacian-48/tmp/1314835934416372000/DistributedMatrix.times.inputVector/

Also, the directory with "diagonal" does not have a number; it assumes it is 
underneath a job-unique path:
{code}
examples/output/calculations:

diagonal        laplacian-48    seqfile-160
{code}
(The jobs create unique directories with a random 8-bit number.)

Any hints on exactly which API call is wrong?

      was (Author: lancenorskog):
    Running DisplaySpectralKMeans gives this error:

FileNotFound:Exception
examples/output/calculations/laplacian-48/tmp/data not found

In fact, the data is stored here:

examples/output/calculations/laplacian-48/tmp/1314835934416372000/DistributedMatrix.times.inputVector/

Any hints on exactly which API call is wrong?
  
> 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: aff.txt, raw.txt, 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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