Announcing Apache Mahout 0.1 #
This morning I received Grant’s release mail of Apache Mahout. I am really happy that after little more than one year
we now have our first release out there to test and scrutinate by anyone interested in the project. Thanks to all the
committers who have helped make this possible. A special thanks to Grant Ingersoll for putting so much time into
getting many release issues out of the way as well as to those who reviewed the release candidates and all the major
and minor problems.
For those who are not familiar with Mahout: The goal of the project is to build a suite of
machine learning libraries under the Apache license. The main focus is on:
- Building a viable community
that develops new features, helps users with software problems and is interested in the data mining problems Mahout
users.
- Developing stable, well documented, scalable software that solves your problems.
The current release includes several algorithms for clustering (k-Means, Canopy, fuzzy k-Means, Dirichlet based), for classification (Naive Bayes and Complementary Naive Bayes). There is some integration with the Watchmaker evolutionary programming framework. The Taste Collaborative Filtering framework moved to Mahout as well. Taste has been around for a while and is much more mature than the rest of the code.
With this being a 0.1 release we are looking for early adopters that are willing to work with cutting edge software and gain benefits from working closely together with the community. We are seeking feedback on use cases as well as performance numbers. If you are using Mahout in your projects or plan to use it or even only evaluate it - we are happy about hearing back from you on our mailing lists. Tell us what you like, what works well, but do not forget to tell us what you would like to improve. Contributions and Patches as always are very welcome.
For more information see the project homepage, especially the wiki and the Lucene weblog by Grant Ingersoll.