The sequential algorithm finds more/better clusters than the mapreduce one.
There's not a huge difference, but the standalone one is better for sure.
Thanks and Regards,
Paritosh
On 03-10-2011 01:47, Konstantin Shmakov wrote:
I'd assume that distributed and sequential algorithms shouldn't produce
identical results. To start with, they differ in initial setup:
-- In distributed algorithm each mapper deals with subset of data and starts
by picking up a random point, so N random points are picked up by N mappers
to start with.
-- In sequential algorithm 1 mapper deals with all data and starts by
picking up 1 random point.
But for the data with real clusters both algorithms should produce similar
results. How different are the results in your case?
Thanks
--Konstantin
On Sun, Oct 2, 2011 at 1:36 AM, Paritosh Ranjan<[email protected]> wrote:
Even run() of CanopyDriver, which takes only T1 and T2 is giving different
results for sequential and mapreduce.
This is preventing me from scaling up, as I need to run mapreduce on hadoop
to scale.
Is anyone having any idea of this problem?
On 02-10-2011 00:27, Paritosh Ranjan wrote:
Hi,
I am able to cluster correctly sequentially, using CanopyDriver.
However, the same dataset, when processed as a MapReduce job, where ( t1 =
t3 and t2 = t4 and t1>t2) is not working. I am getting errors like Canopies
are empty.
I also tried to reduce the values of t3 and t4. But reducing it either has
no effect or gives meaningless results.
Am I doing something wrong? or is there a bug somewhere?
I feel that both, sequential and MapReduce should give similar results.
But, It is not happening.
Thanks and Regards,
Paritosh
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