I think the discrepancy between the number (n=) of vectors reported by
the cluster and the number of points actually clustered by the -cl
option is normal.
* In the final iteration, points are assigned to (observed by)
(classified as) each cluster based upon the distance measure and the
cluster center computed from the previous iteration. The (n=) value
records the number of points "observed by" the cluster in that
iteration.
* After the final iteration, a new cluster center is calculated for
each cluster. This moves the center by some amount, less than the
convergence threshold, but it moves.
* During the subsequent classification (-cl) step, these new centers
are used to classify the points for output. This will inevitably
cause some points to be assigned to (observed by) (classified as) a
different cluster and so the output clusteredPoints will reflect
this final assignment.
In small, contrived examples, the clustering will likely be more stable
between the final iteration and the output of clustered points.
On 9/10/12 9:06 AM, Whitmore, Mattie wrote:
Hi,
I too am having this problem. I have a very small dimension space (3), and a lot of
vectors (hundreds of millions). Therefore I can't print all to disk (I receive an OOM
error). However, I can print 30 sample points easily, and doing so showed results
similar to you (I "named" my vectors to be the number of vectors clusterDumper
printed in the cluster):
VL-50{n=0 c=[...] r=[]}
Weight : [props - optional]: Point:
1.0: 1 = [...]
1.0: 2 = [...]
...
1.0: 10 = [...]
--> note also radius is blank, whereas the points do have spread in all
dimensions, this happened ONLY with converged clusters.
CL-51{n=4 c=[...] r=[...]}
Weight : [props - optional]: Point:
1.0: 1 = [...]
1.0: 2 = [...]
...
1.0: 6 = [...]
As far as I understand the algorithm, problems which arise due to dimensionality are
convergence problems. Basically, distance between points is "longer" as
dimension increases (volume increases dramatically as dimension increases).
This shouldn't affect clusterDumper, as clusterDumper simply reports on
sequence files from a completed job. This is why the discrepancy is not making
a lot of sense to me. Having more vectors within each cluster makes sense --
when I sum the printed n values, I receive a number magnitudes smaller than the
number of vectors I clustered.
I used Mahout v0.7, Hadoop 0.20.2-cdh3u3
-----Original Message-----
From: Yuji NISHIDA@U-Tokyo [mailto:[email protected]]
Sent: Sunday, September 09, 2012 4:46 AM
To: [email protected]
Subject: Re: mahout clusterdump output
Hi all
I still want to confirm that this is not a problem.
Especially the n value, I just hope it is not problematic...
I discussed this in my lab, one of our members noted that the dimension of
feature vectors and the number of vectors I used were very different.
I have used 100 dimensions of vector and 600,000 vectors.
Do you think it may cause some problems if I use both small dimensions and
large number of vectors simultaneously and we need to make sure that there
is relation between them (especially in number)?
Or do you think 100 is too small for the dimension?
I will appreciate very much that someone follows my question.
Regards.
2012/8/4 Yuji NISHIDA@U-Tokyo <[email protected]>:
Dear all
I am working on mahout to use canopy and kmeans and got a problem
about clusterdump output.
Each vector has simple number incremented from 1 as its name.
When I used 5,000 vectors, I got a correct output. It looks like:
VL-0{n=64,c=[...], r[...]}
1.0: 1= [...]
1.0: 3= [...]
1.0: 4= [...]
...
1.0: 396= [...] # The number of vectors is exactly same as n(64).
VL-1{n=5,c=[...], r[...]}
1.0: 2= [...]
1.0: 12= [...]
...
1.0: 4221= [...]
VL-2{n=121,c=[...], r[...]}
...
Each number of vectors in VL is exactly same as its n value.
When I used 600,000 vectors, the output looks wrong like:
VL-0{n=14,c=[...], r[...]}
1.0: 66636= [...]
1.0: 122570= [...]
...
1.0: 522794= [...] # The number of vectors is 31.
VL-8{n=0,c=[...], r[...]}
1.0: 393539= [...]
1.0: 398877= [...]
...
1.0: 513448= [...] # The number of vectors is 5.
VL-16{n=2,c=[...], r[...]}
...
It looks VL-1 to VL-7 and VL-9 to VL-15 are not used but I confirmed
them existing in the output.
It seems using VL in order as 0,8,16,...,11552, 1,9,17,...,11553,
2,10,18... and so on.
Can I believe this result or should I doubt this is caused by some bugs?
Hadoop : 0.20.204
Mahout : rev. 1351561, 1366995, 1367871
Best regards.
--
nishidy@u-tokyo
