The Mahout project was started by several people involved in the Apache Lucene (open source search) COMMUNITY with an active interest in machine learning and a desire for robust, well-documented, scalable implementations of common machine-learning algorithms for clustering and categorization. The community was initially DRIVEN by Ng et al.’s paper “Map-Reduce for Machine Learning on Multicore” (see Resources) but has since evolved to cover much broader machine-learning approaches. Mahout also aims to:

• Build and support a community of users and contributors such that the CODE outlives any PARTICULAR contributor’s involvement or any particular company or university’s funding.
• Focus on real-world, practical use cases as opposed to bleeding-edge research or UNPROVEN techniques.
• Provide quality documentation and examples.

The Mahout project was started by several people involved in the Apache Lucene (open source search) community with an active interest in machine learning and a desire for robust, well-documented, scalable implementations of common machine-learning algorithms for clustering and categorization. The community was initially driven by Ng et al.’s paper “Map-Reduce for Machine Learning on Multicore” (see Resources) but has since evolved to cover much broader machine-learning approaches. Mahout also aims to: