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https://issues.apache.org/jira/browse/MAHOUT-524?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13206256#comment-13206256
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Dan Brickley commented on MAHOUT-524:
-------------------------------------
I just tried spectral k-means with some wikipedia/dbpedia data (1.0 affinities
for every page and topic category URL pair in the Wiki. Data came from
http://downloads.dbpedia.org/3.7/en/article_categories_en.nt.bz2 and is dropped
in the Web at http://danbri.org/2012/spectral/dbpedia/ (I posted .csv plus an
int-to-URL dictionary file).
My best guess at commandline (running this w/ today's trunk + a fresh
0.20.203.0 hadoop pseudo-cluster) was this:
mahout spectralkmeans -i wiki/ -o output1 -k 20 -d 4192499 --maxIter 10
(where hdfs wiki/ subdir contains the .csv data file)
Unfortunately I'm hitting one of the various problems discussed above. If
anyone else can reproduce this, perhaps a fresh JIRA is needed.
It gets stuck after the first job, with an essentially empty seqfile. Full
transcript here: https://gist.github.com/1804016
(checked with "mahout seqdumper --seqFile
output1/calculations/diagonal/part-r-00000")
This is essentially the same experience I had back in Sept (see above) running
a similar test.
> 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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