Mahout for R Users by Simon Raper.
From the post:
I have a few posts coming up on Apache Mahout so I thought it might be useful to share some notes. I came at it as primarily an R coder with some very rusty Java and C++ somewhere in the back of my head so that will be my point of reference. I’ve also included at the bottom some notes for setting up Mahout on Ubuntu.
What is Mahout?
A machine learning library written in Java that is designed to be scalable, i.e. run over very large data sets. It achieves this by ensuring that most of its algorithms are parallelizable (they fit the map-reduce paradigm and therefore can run on Hadoop.) Using Mahout you can do clustering, recommendation, prediction etc. on huge datasets by increasing the number of CPUs it runs over. Any job that you can split up into little jobs that can done at the same time is going to see vast improvements in performance when parallelized.
Like R it’s open source and free!
So why use it?
Should be obvious from the last point. The parallelization trick brings data and tasks that were once beyond the reach of machine learning suddenly into view. But there are other virtues. Java’s strictly object orientated approach is a catalyst to clear thinking (once you get used to it!). And then there is a much shorter path to integration with web technologies. If you are thinking of a product rather than just a one off piece of analysis then this is a good way to go.
Large data sets have been in the news of late. 😉
Are you ready to apply machine learning techniques to large data sets?
And will you be familiar enough with the techniques to spot computational artifacts?
Can’t say for sure but more knowledge of and practice with Mahout might help with those questions.