Adding and revising a little: Apache Mahout 0.2 has been released and is now available for public download at http://www.apache.org/dyn/closer.cgi/lucene/mahout
Up to date maven artifacts can be found in the Apache repository at https://repository.apache.org/content/repositories/releases/org/apache/mahout/ Apache Mahout is a subproject of Apache Lucene with the goal of delivering scalable machine learning algorithm implementations under the Apache license. http://www.apache.org/licenses/LICENSE-2.0 > Mahout is a machine learning library meant to scale to the size of > data we manage today. Built on top of the powerful map/reduce > paradigm of Apache Hadoop project, Mahout lets you run popular > machine learning methods like clustering, collaborative filtering, > classification over Terabytes of data over thousands of computers. > <- We may want to emphasize that using Mahout makes sense also for > those people that do not have clusters with thousands of nodes? Mahout is a machine learning library meant to scale: Scale in terms of community to support anyone interested in using machine learning. Scale in terms of business by providing the library under a commercially friendly, free software license. Scale in terms of computation to the size of data we manage today. Built on top of the powerful map/reduce paradigm of the Apache Hadoop project, Mahout lets you solve popular machine learning problem settings like clustering, collaborative filtering and classification over Terabytes of data over thousands of computers. Implemented with scalability in mind the latest release brings many performance optimizations so that even in a single node setup the library performs well. > <- As mentioned earlier by Grant, we do need performance benchmarks at > least for the the next release to prove that. The complete changelist can be found here: http://issues.apache.org/jira/browse/MAHOUT/fixforversion/12313278 New Mahout 0.2 features include - Major performance enhancements in Collaborative Filtering, Classification and Clustering - New: Latent Dirichlet Allocation(LDA) implementation for topic modelling - New: Frequent Itemset Mining for mining top-k patterns from a list of transactions - New: Decision Forests implementation for Decision Tree classification (In Memory & Partial Data) - New: HBase storage support for Naive Bayes model building and classification - New: Generation of vectors from Text documents for use with Mahout Algorithms - Performance improvements in various Vector implementations - Tons of bug fixes and code cleanup Getting started: New to Mahout? 1) Download Mahout at http://www.apache.org/dyn/closer.cgi/lucene/mahout 2) Check out the Quick start: http://cwiki.apache.org/MAHOUT/quickstart.html 3) Read the Mahout Wiki: http://cwiki.apache.org/MAHOUT 4) Join the community by subscribing to mahout-u...@lucene.apache.org 5) Give back: http://www.apache.org/foundation/getinvolved.html 6) Consider adding yourself to the power by Wiki page: http://cwiki.apache.org/MAHOUT/poweredby.html For more information on Apache Mahout, see http://lucene.apache.org/mahout Additional comment: I suppose, I will copy this over to my personal blog once the release is out. I would like to invite those interested in or using Mahout to do so as well.
