Mahout was introduced in Chapter 2, Java Libraries and Platforms for Machine Learning, as a scalable machine learning library. It provides a rich set of components with which you can construct a customized recommendation system from a selection of algorithms. The creators of Mahout say that it is designed to be enterprise-ready; it's designed for performance, scalability, and flexibility.
Mahout can be configured to run in two flavors: with or without Hadoop, and for a single machine and distributed processing, respectively. We will focus on configuring Mahout without Hadoop. For more advanced configurations and further uses of Mahout, I would recommend two recent books: Learning Apache Mahout, by Chandramani Tiwary, Packt Publishing, and Learning Apache Mahout Classification, by Ashish Gupta, Packt Publishing.
As Apache Mahout's build and release...
