- Paperback: 416 pages
- Publisher: Manning Publications (October 17, 2011)
- Language: English
- ISBN-10: 1935182684
- ISBN-13: 978-1935182689
- Product Dimensions: 7.4 x 0.9 x 9.2 inches
- Shipping Weight: 1.6 pounds (View shipping rates and policies)
- Average Customer Review: 16 customer reviews
- Amazon Best Sellers Rank: #1,098,828 in Books (See Top 100 in Books)
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Mahout in Action
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About the Author
Sean Owen has been a practicing software engineer for 9 years, most recently at Google, where he helped build and launch Mobile Web search. He joined Apache's Mahout machine learning project in 2008 as a primary committer and works as a Mahout consultant.
Robin Anil joined Apache's Mahout project as a Google Summer of Code student in 2008 and contributed to the Classifier and Frequent Pattern Mining packages with algorithms that run on the Hadoop Map/Reduce platform. Since 2009, he has been a committer at Mahout and works as a full-time Software Engineer at Google.
Ted Dunning is Chief Application Architect at MapR Technologies and committer and PMC member for the Apache Mahout project. He contributing to the Mahout clustering, classification and matrix decomposition algorithms. He was the chief architect behind the MusicMatch (now Yahoo Music) and Veoh recommendation systems, and built fraud detection systems for ID Analytics.
Ellen Friedman is an experienced writer with a doctorate in biochemistry. In addition to a research career, she has written on a wide range of scientific and technical topics including molecular biology, medicine and earth science.
Top customer reviews
Once I'd begun working, I started running into all kinds of questions about anonymous users, content-based recommendations, DataModel formats, and all kinds of other stuff. I looked and looked around online and had trouble finding concise answers to my questions.
I picked Mahout in Action back up and started reading again - and it was like Sean Owen could read my mind. I've never before purchased a computer science book that answered so many practical questions that were so hard to find online. So many small sections provide really great answers to specific problems that arise while using Mahout, and the index is great for finding those sections quickly. The code examples were perfect for jump-starting my code base.
Today I finished my initial prototype and everything is working exactly how I envisioned it when I first set out to build the recommender system. Would not have happened without this book.
Mahout in Action is written and explained so well with simple real life explanations and definitely executable code that you can gather all the techniques you've heard/read about come right near your grasp. Just extend your arms and reach for that recommender or clusterer.
A big thanks to every Mahout contributor and double thanks to the authors.
Oh by the way! Order the book. At whatever price, this will save you hundreds of hours of reading and coding.
First the book use 0.5 of mahout while version 0.7 is the current release. Much of the code no longer works from the examples. Keeping the example code updated on the site would be a huge plus.
A useful start would have been discussing theory in chapter 7. Instead the theory is discussed in chapter 9.
Chatper 7 is a mishmash of distance measures, similarity and examples.
A thorough explanation of the output produced by clusterdumper would have been useful. With some knowledge of the algorithm you can figure out what c and r are and the numbers assigned to the vectors are. But taking a simple example and showing the actually hand calculation would be very useful to someone totaly new to clustering .
I don't like to be overly critical, the book has some good information, but its much more difficult to extract it than it should be.