|Developer(s)||Apache Software Foundation|
|Stable release||0.8 / 25 July 2013|
|License||Apache 2.0 Licence|
Apache Mahout is a project of the Apache Software Foundation to produce free implementations of distributed or otherwise scalable machine learning algorithms focused primarily in the areas of collaborative filtering, clustering and classification. Many of the implementations use the Apache Hadoop platform. Mahout also provides Java libraries for common math operations (focused on linear algebra and statistics) and primitive Java collections. Mahout is a work in progress; the number of implemented algorithms has grown quickly, but various algorithms are still 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.[when?]
- "Introducing Apache Mahout". ibm.com. 2011 [last update]. Retrieved 13 September 2011.
- "InfoQ: Apache Mahout: Highly Scalable Machine Learning Algorithms". infoq.com. 2011 [last update]. Retrieved 13 September 2011.
- "Algorithms - Apache Mahout - Apache Software Foundation". cwiki.apache.org. 2011 [last update]. Retrieved 13 September 2011.
- Official website
- 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.
- A Spring based Java demo application that demonstrates a simple recommender using Apache Mahout
- Demo of travel recommendations using anonymous user-based recommender of Mahout