I’m pleased to announce the first release of the Apache Mahout project.  Apache Mahout is a suite of machine learning algorithm implementations.  Here’s the release notice I just sent to various mailing lists:

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

The release contents have been pushed out to the main Apache release
site and the m2 ibiblio sync repository.

Apache Mahout 0.1 is the project’s first release and is focused on establishing a baseline release while
attracting more contributors. Details can
be found in JIRA:


Apache Mahout is available in source form from the following download page:

Apache Mahout is also available for Maven 2 users via
the Central Maven Repositories:

When downloading from a mirror site, please remember to verify the downloads
using signatures found on the Apache site:

For more information on Apache Mahout, visit the project home page: