Book Image

Hadoop Real-World Solutions Cookbook - Second Edition

By : Tanmay Deshpande
Book Image

Hadoop Real-World Solutions Cookbook - Second Edition

By: Tanmay Deshpande

Overview of this book

Big data is the current requirement. Most organizations produce huge amount of data every day. With the arrival of Hadoop-like tools, it has become easier for everyone to solve big data problems with great efficiency and at minimal cost. Grasping Machine Learning techniques will help you greatly in building predictive models and using this data to make the right decisions for your organization. Hadoop Real World Solutions Cookbook gives readers insights into learning and mastering big data via recipes. The book not only clarifies most big data tools in the market but also provides best practices for using them. The book provides recipes that are based on the latest versions of Apache Hadoop 2.X, YARN, Hive, Pig, Sqoop, Flume, Apache Spark, Mahout and many more such ecosystem tools. This real-world-solution cookbook is packed with handy recipes you can apply to your own everyday issues. Each chapter provides in-depth recipes that can be referenced easily. This book provides detailed practices on the latest technologies such as YARN and Apache Spark. Readers will be able to consider themselves as big data experts on completion of this book. This guide is an invaluable tutorial if you are planning to implement a big data warehouse for your business.
Table of Contents (18 chapters)
Hadoop Real-World Solutions Cookbook Second Edition
Credits
About the Author
Acknowledgements
About the Reviewer
www.PacktPub.com
Preface
Index

Clustering text data using K-Means


In this recipe, we are going to take a look at how to use Mahout to cluster text data using Mahout's implementation of the K-Means algorithm. K-Means is very popular clustering algorithm; you can read more about it at https://en.wikipedia.org/wiki/K-means_clustering.

Getting ready

To perform this recipe, you should have a running Hadoop cluster as well as the latest version of Mahout installed on it.

How to do it...

In this recipe, we are going to use Mahout's K Means algorithm to cluster the text data that is available. To do this, we first need to get some text data and copy it to HDFS:

hadoop fs –mkdir /kmeans
hadoop fs –put mydata.txt /kmeans/input

In order to execute the K-Means job on the given data, we first need to convert it into sequential files and from these sequential files to TF-IDF vectors. Mahout provides built-in utilities to perform these actions. The following are the commands to do this.

To convert text data into a sequential file, here is...