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Apache Mahout 0.1 Released


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via Grant's Grunts: Lucene Edition by grant_ingersoll on 4/7/09

Whew.  I just sent out the announcement for the release of Apache Mahout 0.1.  Congrats and thanks to everyone in Mahout land for all their hard work, especially all the other committers.

For those who don't know about Mahout, it's primary goal is to develop a suite of machine learning libraries under the Apache license.  Other goals (in no particular order) include:

  1. Develop community around the code (always key at the ASF)
  2. Create compelling documentation and demos
  3. Create scalable, commercially viable implementations of popular, well-understood algorithms

We have several clustering algorithm implementations: k-Means, fuzzy k-Means, Dirichlet, Mean-Shift, Canopy.  We also have implementations of naive bayes and complementary naive bayes for classification and some integration with the Watchmaker evolutionary programming framework.  Finally, we have incorporated the Taste Collaborative Filtering project into Mahout as well.  Taste has been around for a while and is much more mature than the rest of the code, but still makes for a nice fit.

Naturally, this is a 0.1 release, so what we are looking for are people willing to work on the edge just a little bit.  While we have confidence in our release, we are definitely looking for feedback about performance, use cases, etc.  Most importantly, we are looking to add to the community via contributions, etc.  This is always a key factor in any ASF project, and Mahout is no different.

To learn more, visit: http://lucene.apache.org/mahout and especially the wiki.


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