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From "Divya" <>
Subject RE: (Near) Realtime clustering
Date Wed, 24 Nov 2010 02:32:11 GMT

Even I also have similar requirement.
Can some one please provide me the steps of hybrid approach.


-----Original Message-----
From: Jeff Eastman [] 
Sent: Wednesday, November 24, 2010 2:19 AM
Subject: RE: (Near) Realtime clustering

I'd suggest a hybrid approach: Run the batch clustering periodically over
the entire corpus to update the cluster centers and then use those centers
for real-time clustering (classification) of new documents as they arrive.
You can use the sequential execution mode of the clustering job to classify
documents in real-time. This will suffer from the fact that new news topics
will not immediately materialize new clusters until the batch job runs

-----Original Message-----
From: Gustavo Fernandes [] 
Sent: Tuesday, November 23, 2010 9:58 AM
Subject: (Near) Realtime clustering

Hello, we have a mission to implement a system to cluster news articles in
near real time mode. We have a large amount of articles (millions), and we
started using k-means to created clusters based on a fixed value of "k". The
problem is that we have a constant incoming flow of news articles and we
can't afford to rely on a batch process, we need to be able to present users
clustered articles as soon as they arrive in our database. So far our
clusters are saved into a SequenceFile, as normally output by k-means
What would be the recommended way of approaching this problem with Mahout?
Is it possible to manipulate the generated clusters and incrementally add
new articles to them, or even forming new clusters without incurring the
penalty of recalculating for every vector again? Is starting with k-means
the right way? What would be the right combination of algorithms to provide
incremental and fast clustering calculation?


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