Apache Mahout is an Apache project to produce free implementations of distributed or otherwise scalable machine learning algorithms on the Hadoop platform.^[1][2] Mahout is a work in progress; the number of implemented algorithms has grown quickly,^[3] but there are still various algorithms missing.

While Mahout's core algorithms for clustering, classification and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm, it does not restrict contributions to Hadoop based implementations. Contributions that run on a single node or on a non-Hadoop cluster are also welcomed. For example, the 'Taste' collaborative-filtering recommender component of Mahout was originally a separate project, and can run stand-alone without Hadoop. Integration with initiatives such as the Pregel-like Giraph are actively under discussion.

References

External links

• EC2 AMI with Hadoop and Mahout
• Giraph - a Graph processing infrastructure that runs on Hadoop (see Pregel).
• Pregel - Google's internal graph processing platform, released details in ACM paper.