Apache Mahout Cookbook
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.
|Course Length||7 hours 30 minutes|
|Date Of Publication||26 Dec 2013|
|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|