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Apache Mahout Cookbook [Kindle Edition]

Piero Giacomelli

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In Detail

The rise of the Internet and social networks has created a new demand for software that can analyze large datasets that can scale up to 10 billion rows. Apache Hadoop has been created to handle such heavy computational tasks. Mahout gained recognition for providing data mining classification algorithms that can be used with such kind of datasets.

"Apache Mahout Cookbook" provides a fresh, scope-oriented approach to the Mahout world for both beginners as well as advanced users. The book gives an insight on how to write different data mining algorithms to be used in the Hadoop environment and choose the best one suiting the task in hand.

"Apache Mahout Cookbook" looks at the various Mahout algorithms available, and gives the reader a fresh solution-centered approach on how to solve different data mining tasks. The recipes start easy but get progressively complicated. A step-by-step approach will guide the developer in the different tasks involved in mining a huge dataset. You will also learn how to code your Mahout’s data mining algorithm to determine the best one for a particular task. Coupled with this, a whole chapter is dedicated to loading data into Mahout from an external RDMS system. A lot of attention has also been put on using your data mining algorithm inside your code so as to be able to use it in an Hadoop environment. Theoretical aspects of the algorithms are covered for information purposes, but every chapter is written to allow the developer to get into the code as quickly and smoothly as possible. This means that with every recipe, the book provides the code for reusing it using Maven as well as the Maven Mahout source code.

By the end of this book you will be able to code your procedure to do various data mining tasks with different algorithms and to evaluate and choose the best ones for your tasks.


"Apache Mahout Cookbook" uses over 35 recipes packed with illustrations and real-world examples to help beginners as well as advanced programmers get acquainted with the features of Mahout.

Who this book is for

"Apache Mahout Cookbook" is great for developers who want to have a fresh and fast introduction to Mahout coding. No previous knowledge of Mahout is required, and even skilled developers or system administrators will benefit from the various recipes presented.

Über den Autor und weitere Mitwirkende

Piero Giacomelli

Piero Giacomelli started playing with computers back in 1986 when he received his first PC (a commodore 64). Despite his love for computers, he graduated in Mathematics, entered the professional software industry in 1997, and started using Java.

He has been involved in a lot of software projects using Java, .NET, and PHP. He is not only a great fan of JBoss and Apache technologies, but also uses Microsoft technologies without moral issues.

He has worked in many different industrial sectors, such as aerospace, ISP, textile and plastic manufacturing, and e-health association, both as a software developer and as an IT manager. He has also been involved in many EU research-funded projects in FP7 EU programs, such as CHRONIOUS, I-DONT-FALL, FEARLESS, and CHROMED.

In recent years, he has published some papers on scientific journals and has been awarded two best paper awards by the International Academy, Research and Industry Association (IARIA).

In 2012, he published HornetQ Messaging Developer's Guide, Packt Publishing, which is a standard reference book for the Apache HornetQ Framework.

He is married with two kids, and in his spare time, he regresses to his infancy ages to play with toys and his kids.


  • Format: Kindle Edition
  • Dateigröße: 6774 KB
  • Seitenzahl der Print-Ausgabe: 252 Seiten
  • ISBN-Quelle für Seitenzahl: 1849518025
  • Verlag: Packt Publishing (26. Dezember 2013)
  • Verkauf durch: Amazon Media EU S.à r.l.
  • Sprache: Englisch
  • ASIN: B00HJR6R86
  • Text-to-Speech (Vorlesemodus): Aktiviert
  • X-Ray:
  • Word Wise: Nicht aktiviert
  • Amazon Bestseller-Rang: #284.361 Bezahlt in Kindle-Shop (Siehe Top 100 Bezahlt in Kindle-Shop)

  •  Ist der Verkauf dieses Produkts für Sie nicht akzeptabel?

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Die hilfreichsten Kundenrezensionen auf (beta) 2.5 von 5 Sternen  8 Rezensionen
2 von 2 Kunden fanden die folgende Rezension hilfreich
1.0 von 5 Sternen Basic and wrong focused. 3. März 2014
Von German Araujo - Veröffentlicht auf
Format:Kindle Edition|Verifizierter Kauf
The level of the book is very low, every interesting topic is stated as "out of the scope". The main part of the book is wasted in explaining (more than once) meaningless topics like: how to create a project in netbeans, hot to uncompress a tar file, or even how to write a csv file using java!!
Thumbs down for sure...
1 von 1 Kunden fanden die folgende Rezension hilfreich
2.0 von 5 Sternen Nothing better than web tutorial. 7. Januar 2014
Von Jlee - Veröffentlicht auf
Format:Kindle Edition|Verifizierter Kauf
This book contains very basic features.
It's good for beginners who want quick tour over mahout functionality and use cases, however, she/he will need more precise references to create something practical.
I expected more precise use case and data handling technique, such as scaling mahout algorithm with map reduce.
4.0 von 5 Sternen Beginner intro to mahout with very interesting examples 4. April 2014
Von Christian S. - Veröffentlicht auf
Definitely it is a good book with very interesting examples.
The code in Java is very basic and easy to follow, it is intended for beginner programmers.

This is a book about practical examples with Mahout, and as the author notes, his intention was not to teach the theory of the machine learning algorithms.

To know more in detail about the algorithms and understand more about machine learning you will have to follow the external references and papers that the author linked in each chapter.

The first chapter deals with the environment and setup.

The setup is very concise and clear. I downloaded more recent versions of the libraries indicated in the book and the same installation steps were still valid. The versions I ended up using were:

NetBeans IDE 8.0
Java 1.7.0_51
Maven 3.0.5
Hadoop 0.23.5

The current version of Mahout is 0.9 and in that version they removed the SlopOneRecommender class, so the first example doesn’t work if you checked out HEAD from trunk. It is better to check out the previous version 0.8 to follow the examples and code from the book. It is really nice to finish the first chapter running a real example.

In chapters 2 and 3 the author explores the storage options we have to load and save our datasets. This is done using sequential files as we are used in Hadoop or using Sqoop to import data from DB into the Hadoop file system.

From chapter 4 the fun starts with an example of a machine learning algorithm in each chapter.

I like the approach of using first the algorithm from command line with predefined parameters and then reproduce it later with a more detailed implementation in Java.

One of the nice things about those examples is that the datasets. Those come from real life data. In particular I liked the stock market forecasting from chapter 5 and the spectral clustering of medical images from chapter 7. This last one showing the author experience in that field.

What I didn’t like about the book are 2 things, first the layout for the code was not that good, making the code hard to read when wrapping lines. The second one is the typeface for the mathematical equations which definitely don’t look good.

In summary, It’s been a very entertaining book with realistic and practical mahout examples.
3.0 von 5 Sternen Useful but lots of typos 8. Januar 2014
Von Sujit Pal - Veröffentlicht auf
This book covers the current (0.8) version of Apache Mahout. The only other Mahout book (Mahout in Action) covers a much earlier version, and since Mahout code has so much churn that even the online documentation is frequently out of date, it is uniquely positioned to educate people who are new to Mahout or unaware of all its capabilities. The book's coverage is fairly comprehensive, it attempts to cover all the functionality available in the current Mahout, as well as functionality (Genetic Algorithms) that have been deprecated (but can still be accessed using an older version). Each algorithm is explained in words, command line usage, and Java code to call the sequential and/or parallel implementation as applicable. However, the book is full of typos that can leave the reader somewhat confused (for example, the book asserts that the square root of 500,000 is 707,11 where it actually meant 707.11. In another instance, it asserts that rows in a 600 row x 60 column dataset need to be transformed into a 600 element vector when it actually meant 60). The book would also benefit if the sentence structures were reviewed by a native English speaker. So, if you are willing to think critically as you are reading and mentally ignore the typos and the language, then the book can be useful, otherwise you should wait for the second edition of this book when hopefully these things will be corrected. I would have given the book 5-stars based on content if it didn't have so many typos.
4.0 von 5 Sternen Great book for beginners in both Mahout and Machine Learning 31. Januar 2014
Von Pavan K Narayanan - Veröffentlicht auf
Very well written for Developers who are new to both Mahout and Machine Learning, with walk-throughs and screenshots. However, if you have experience in writing heuristics/have expertise in Machine learning, you can skip this book. Concise and to the point, few clerical errors and typos, though. The reader should be intelligent enough to understand RDBMS and RDMS programming are the same.

This book certainly makes a wonderful academic companion if anyone plan to use Mahout in their academic research project. Recipes like import/export data from HDFS/RDBMS, spectral clustering are a highlight. The author does not assume that the user is familiar with MySQL so there is a walkthrough on installing the same. I'd skip the chapter 10 completely as Mahout does not support TSP (GP) anymore.

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