Mahout in Action

Mahout in Action is a hands-on introduction to machine learning with Apache Mahout. Following real-world examples, the book presents practical use cases and then illustrates how Mahout can be applied to solve them. Includes a free audio- and video-enhanced ebook.

A computer system that learns and adapts as it collects data can be really powerful. Mahout, Apache's open source machine learning project, captures the core algorithms of recommendation systems, classification, and clustering in ready-to-use, scalable libraries. With Mahout, you can immediately apply to your own projects the machine learning techniques that drive Amazon, Netflix, and others.

This book covers machine learning using Apache Mahout. Based on experience with real-world applications, it introduces practical use cases and illustrates how Mahout can be applied to solve them. It places particular focus on issues of scalability and how to apply these techniques against large data sets using the Apache Hadoop framework.

This book is written for developers familiar with Java. No prior experience with Mahout is assumed.

• Use group data to make individual recommendations
• Find logical clusters within your data
• Filter and refine with on-the-fly classification
• Free audio and video extras

This book is written for developers familiar with Java. No prior experience with Mahout is assumed.

Sean Owen helped build Google's Mobile Web search and launched the Taste framework, now part of Mahout. Robin Anil contributed the Bayes classifier and frequent pattern mining implementations to Mahout. Ted Dunning contributed to the Mahout clustering, classification, and matrix decomposition algorithms. Ellen Friedman is an experienced writer with a doctorate in biochemistry.