Apache Mahout 0.1 Released

Apache Mahout 0.1 has been released. Apache Mahout is a project which attempts to make machine learning both scalable and accessible. It is a sub-project of the excellent Apache Lucene project which provides open source search software.

This is also the first public release of Taste collaborative filtering project ever since it was donated to Apache Mahout last year.

From the official announce email:

The Apache Lucene project is pleased to announce the release of Apache Mahout 0.1. Apache Mahout is a subproject of Apache Lucene with the goal of delivering scalable machine learning algorithm implementations under the Apache license. The first public release includes implementations for clustering, classification, collaborative filtering and evolutionary programming.

Highlights include:

1. Taste Collaborative Filtering
2. Several distributed clustering implementations: k-Means, Fuzzy k-Means, Dirchlet, Mean-Shift and Canopy
3. Distributed Naive Bayes and Complementary Naive Bayes classification implementations
4. Distributed fitness function implementation for the Watchmaker evolutionary programming library
5. Most implementations are built on top of Apache Hadoop (http://hadoop.apache.org) for scalability

Look at the announcement for more details - http://www.nabble.com/-ANNOUNCE--Apache-Mahout-0.1-Released-td22937220.html

There is a lot of interest in Mahout from the community and it had a successful year with the Google Summer of Code 2008 program. This year again, there have been multiple proposals and I'm sure that great things are on the way.

The Apache Mahout Wiki has a lot of good documentation on the project as well as on machine learning in general. Their mailing list is very active and of course, they have some great people involved, see the committers page. I would encourage every student interested in machine learning to participate in the project.

I wish good luck to the project and the people involved in it. Keep up the great work!