Apache Mahout Cookbook
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- 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
Book DetailsLanguage : English
Paperback : 300 pages [ 235mm x 191mm ]
Release Date : December 2013
ISBN : 1849518025
ISBN 13 : 9781849518024
Author(s) : Piero Giacomelli
Topics and Technologies : All Books, Big Data and Business Intelligence, Data, Cookbooks, Open Source
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What you will learn from this book
- 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
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.