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I'm starting a research about parallel clustering. I see a ton of articles on this topic, so that I don't know where to start. I'd like to get familiar with classic methods of parallelizing clustering. Are there any "have-to-know" books/papers in this area, that would provide me a good introduction?

I'm interested in shared-nothing architecture, generally something that may be suitable for map-reduce model.

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You may be interested to this survey done by a Ph.D. student, which is updated to 2009 and presents classical work on parallel clustering. The survey is full of references you may then read to delve into the gory details.

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There's been recent work on doing clustering in the MRC model (a formal model for analyzing mapreduce computations). Specifically, you should look at the work by Bahmani et al in VLDB 2012 on k-means$||$ and earlier work by Ene et al in KDD 2011 on the same topic. These papers have some discussion of the general problem of parallelizing $k$-means, which might be a good jumping off point for what you're interested in.

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