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Posted to dev@mahout.apache.org by "Jeff Eastman (JIRA)" <ji...@apache.org> on 2010/10/12 05:33:33 UTC
[jira] Created: (MAHOUT-524) DisplaySpectralKMeans example fails
DisplaySpectralKMeans example fails
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Key: MAHOUT-524
URL: https://issues.apache.org/jira/browse/MAHOUT-524
Project: Mahout
Issue Type: Bug
Components: Clustering
Affects Versions: 0.4
Reporter: Jeff Eastman
Fix For: 0.4
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 it gets remarkably far through, finally failing on W.transpose() after the eigen cleanup. I can't imagine this would all be pilot error so I'm opening an issue to track it.
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[jira] Commented: (MAHOUT-524) DisplaySpectralKMeans example fails
Posted by "Hudson (JIRA)" <ji...@apache.org>.
[ https://issues.apache.org/jira/browse/MAHOUT-524?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12920226#action_12920226 ]
Hudson commented on MAHOUT-524:
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Integrated in Mahout-Quality #392 (See [https://hudson.apache.org/hudson/job/Mahout-Quality/392/])
MAHOUT-524: Moved numEigensWritten initialization out of loop. SpectralKMeans now runs to completion but display routing is expecting a 2-d vector and is getting a 5-d vector. Not clustering the original input points. More to test but CleanEigensJob is working.
> 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
> Reporter: Jeff Eastman
> Fix For: 0.4
>
>
> 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 it gets remarkably far through, finally failing on W.transpose() after the eigen cleanup. I can't imagine this would all be pilot error so I'm opening an issue to track it.
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[jira] Updated: (MAHOUT-524) DisplaySpectralKMeans example fails
Posted by "Jeff Eastman (JIRA)" <ji...@apache.org>.
[ https://issues.apache.org/jira/browse/MAHOUT-524?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Jeff Eastman updated MAHOUT-524:
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Description: 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. (was: 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 it gets remarkably far through, finally failing on W.transpose() after the eigen cleanup. I can't imagine this would all be pilot error so I'm opening an issue to track it. )
Fix Version/s: (was: 0.4)
0.5
This does not impact 0.4 usability as spectral clustering is still experimental and needs I/O. The display routine could be removed for hygiene but I prefer to leave it in with a caveat that it is part of several work-in-progress issues to integrate spectral clustering into the rest of the clustering portfolio.
> 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
> Reporter: Jeff Eastman
> Fix For: 0.5
>
>
> 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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