Apache Mahout Cookbook

More Information
Learn
  • Configure from scratch a full development environment for Mahout with NetBeans and Maven
  • Handle sequencefiles for better performance
  • Query and store results into an RDBMS system with SQOOP
  • Use logistic regression to predict the next step
  • Understand text mining of raw data with Naïve Bayes
  • Create and understand clusters
  • Customize Mahout to evaluate different cluster algorithms
  • Use the mapreduce approach to solve real world data mining problems
About

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.

Features
  • Learn how to set up a Mahout development environment
  • Start testing Mahout in a standalone Hadoop cluster
  • Learn to find stock market direction using logistic regression
  • Over 35 recipes with real-world examples to help both skilled and the non-skilled developers get the hang of the different features of Mahout
Page Count 250
Course Length 7 hours 30 minutes
ISBN 9781849518024
Date Of Publication 26 Dec 2013
Introduction
Using the Mahout text classifier to demonstrate the basic use case
Using the Naïve Bayes classifier from code
Using Complementary Naïve Bayes from the command line
Coding the Complementary Naïve Bayes classifier

Authors

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.