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Mahout in Action

15 customer reviews
ISBN-13: 978-1935182689
ISBN-10: 1935182684
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Editorial Reviews

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.


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Product Details

  • 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: 3.9 out of 5 stars  See all reviews (15 customer reviews)
  • Amazon Best Sellers Rank: #396,424 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

13 of 14 people found the following review helpful By Ayon Sinha on October 20, 2011
Format: Paperback Verified Purchase
I have a large scale production code background and have been slowly getting deeper and deeper into recommenders, classification & clustering due to the nature of our business. The Data Mining textbooks have a very different objective, which is to cover every technique so that the person taking the class knows ins and outs of these.
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.
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9 of 11 people found the following review helpful By Alexey Ott on October 15, 2011
Format: Paperback
If you're interested in large scale machine learning, then this book is for you.
This book doesn't provide deep coverage of theoretical foundations of machine learning (I would recommend to look to other books, like Introduction to Machine Learning (Adaptive Computation and Machine Learning series), Machine Learning in Action or Programming Collective Intelligence: Building Smart Web 2.0 Applications, etc., if you want to get more background), but concentrates on explanation on how to use Apache Mahout ([...]) to solve some of machine learning problems: making recommendations, data clustering & classification.

For each of class of these problems, description starts with base things, and continues with more complex examples, including complete solutions, that could be easily adapted for your machine learning problems. All examples that come with book were checked with actual release of Apache Mahout (version 0.5).

Book is written in succinct, but understandable language and provides many code snippets that make understanding of topics much easier. Interesting solution in e-book version of Mahout in Action, is inclusion of audio & video snippets, that explains and/or show "hard places". There is also interesting description of one of Mahout's deployments in real world, where it's used in e-commerce.

So I recommend this book if you're interested in solving machine learning problems that works with very large data sets.
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9 of 11 people found the following review helpful By Kevin M Lanaghan on October 11, 2012
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There's a very incomplete and incoherent feel to the clustering section of this book. Mahout as a command line program vs mahout as a library of routines. A bit of theory thrown in here and there.

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.
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3 of 3 people found the following review helpful By Sujit Pal on December 26, 2012
Format: Paperback Verified Purchase
Apache Mahout is a very active, very useful and very successful open-source project whose focus is to provide machine learning algorithms on top of Hadoop. Because of the activity, however, this book is doomed to appear dated by the time you read it. The book can be useful if you are looking to get an overview of whats available in Mahout and general guidelines on how to use it. Since the project's focus is on providing ready-to-run implementations that can be run from a command line, the book can also be useful for the "under the hood" information it provides about how to use Mahout as an API, ie, use the components as building blocks for your own ready-to-run big data ML application. However, I found (quite drastic at times) differences between the book version (0.6) and what I was using (0.7), both for script parameters as well as Java API. So be prepared to read the project wiki (for the script parameters) and the source code (for using as an API).
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2 of 2 people found the following review helpful By Michael on February 16, 2014
Format: Paperback Verified Purchase
I am brand new to learning algorithms, so I was worried about trying to read this book. First I tried reading some documentation on the Mahout website, but I felt like I was getting a run-around treatment there. Their website kept providing links to documentation that required already knowing how these learning algorithms worked. Eventually I gave this book a try.

I was delightfully surprised, this book covers a lot of the learning algorithms in thorough detail. It is great for people with no prior knowledge of how machine learning works, like I was. If you already understand some things about machine learning, you will probably get bored fast.

I did have a few gripes though:

I felt like the clustering chapters did a great job explaining the k-means algorithm, but just did a little hand-waving for the more advanced algorithms. For example, the explanation of the canopy algorithm did not make sense to me after reading it twice, and I feel like the Latent Dirichlet Analysis algorithm made no sense at all. I learned what these algorithms were good for, but still don't completely understand how they work under the hood. Perhaps they are just too complicated to explain in the book, maybe they belong in an appendix, I don't know.

I'm reading the Classification chapters now, and I must admit that it's a bit verbose. The authors are repeating themselves way too much in chapter 13. I think multiple authors contributed to chapter 13 without looking at each-other's work. On the plus side I feel like I understand it.

I have not tried doing anything yet, some other reviewer's said the examples are out of date.
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