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https://issues.apache.org/jira/browse/MAHOUT-15?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Jeff Eastman updated MAHOUT-15:
-------------------------------
Attachment: MAHOUT-15c.patch
Found another defect in the iteration loop. The order of the done test (done =
done && migrate(0.5)) was omitting the canopy migrations once the first one
reported not done. I reversed the elements and now the algorithm converges in 4
iterations vs 44. I also tweaked the actual migration routine to merge with
only the closest canopy vs. the first one encountered. Finally, I added another
set of values ('/') to the initial image data set and the algorithm clustered
it correctly too:
ABBBBBBBBC
BABBBBBBCB
BBABBBBCBB
BBBABBCBBB
BBBBACBBBB
BBBBCABBBB
BBBCBBABBB
BBCBBBBABB
BCBBBBBBAB
CBBBBBBBBA
Note: The values I added had a z=4 value and were clustered separately (C).
When I changed their z value to 9, there were only two remaining canopies (A,
B):
ABBBBBBBBA
BABBBBBBAB
BBABBBBABB
BBBABBABBB
BBBBAABBBB
BBBBAABBBB
BBBABBABBB
BBABBBBABB
BABBBBBBAB
ABBBBBBBBA
I still do not know what to call this algorithm, perhaps 'colliding canopies'
or 'coalescing canopies'? Though it has some similarity to mean shift I'd be
surprised if the term applies.
> Investigate Mean Shift Clustering
> ---------------------------------
>
> Key: MAHOUT-15
> URL: https://issues.apache.org/jira/browse/MAHOUT-15
> Project: Mahout
> Issue Type: New Feature
> Components: Clustering
> Reporter: Jeff Eastman
> Assignee: Jeff Eastman
> Attachments: MAHOUT-15a.patch, MAHOUT-15b.patch, MAHOUT-15c.patch
>
>
> "The mean shift algorithm is a nonparametric clustering technique which does
> not require prior knowledge of the number of clusters, and does not constrain
> the shape of the clusters."
> http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/TUZEL1/MeanShift.pdf
> Investigate implementing mean shift clustering using Hadoop
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