Mahout in Action

Sean Owen, B. Ellen Friedman, Robin Anil, Ted Dunning

• Publisher: Manning
• Year: 2011
• Print ISBN: 9781935182689
• eBook ISBN: 9781638355373
• Topic: Computer Science
• Subtopic: Data Mining
• Index: Computer Science

Summary 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.
About the Technology
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.
About this Book
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.Owners of a Manning pBook purchased anywhere in the world can download a free eBook from manning.com at any time. They can do so multiple times and in any or all formats available (PDF, ePub or Kindle). To do so, customers must register their printed copy on Manning's site by creating a user account and then following instructions printed on the pBook registration insert at the front of the book.
What's Inside

• 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

Table of Contents

• Meet Apache Mahout
• PART 1 RECOMMENDATIONS
• Introducing recommenders
• Representing recommender data
• Making recommendations
• Taking recommenders to production
• Distributing recommendation computations
• PART 2 CLUSTERING
• Introduction to clustering
• Representing data
• Clustering algorithms in Mahout
• Evaluating and improving clustering quality
• Taking clustering to production
• Real-world applications of clustering
• PART 3 CLASSIFICATION
• Introduction to classification
• Training a classifier
• Evaluating and tuning a classifier
• Deploying a classifier
• Case study: Shop It To Me

Table of contents

• Copyright
• Brief Table of Contents
• Table of Contents
• Preface
• Acknowledgments
• About this Book
• About Multimedia Extras
• About the Cover Illustration
• Chapter 1. Meet Apache Mahout
• Part 1. Recommendations
• Chapter 2. Introducing recommenders
• Chapter 3. Representing recommender data
• Chapter 4. Making recommendations
• Chapter 5. Taking recommenders to production
• Chapter 6. Distributing recommendation computations
• Part 2. Clustering
• Chapter 7. Introduction to clustering
• Chapter 8. Representing data
• Chapter 9. Clustering algorithms in Mahout
• Chapter 10. Evaluating and improving clustering quality
• Chapter 11. Taking clustering to production
• Chapter 12. Real-world applications of clustering
• Part 3. Classification
• Chapter 13. Introduction to classification
• Chapter 14. Training a classifier
• Chapter 15. Evaluating and tuning a classifier
• Chapter 16. Deploying a classifier
• Chapter 17. Case study: Shop It To Me
• Appendix A. JVM tuning
• Appendix B. Mahout math
• Appendix C. Resources
• Index
• List of Figures
• List of Tables
• List of Listings