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- Herausgeber: Packt Publishing
- Kategorie: Fachliteratur
- Sprache: Englisch
As more and more organizations are discovering the use of big data analytics, interest in platforms that provide storage, computation, and analytic capabilities has increased. Apache Mahout caters to this need and paves the way for the implementation of complex algorithms in the field of machine learning to better analyse your data and get useful insights into it.
Starting with the introduction of clustering algorithms, this book provides an insight into Apache Mahout and different algorithms it uses for clustering data. It provides a general introduction of the algorithms, such as K-Means, Fuzzy K-Means, StreamingKMeans, and how to use Mahout to cluster your data using a particular algorithm. You will study the different types of clustering and learn how to use Apache Mahout with real world data sets to implement and evaluate your clusters.
This book will discuss about cluster improvement and visualization using Mahout APIs and also explore model-based clustering and topic modelling using Dirichlet process. Finally, you will learn how to build and deploy a model for production use.
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Seitenzahl: 116
Veröffentlichungsjahr: 2015
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Table of Contents
Apache Mahout Clustering Designs
Apache Mahout Clustering Designs
Copyright © 2015 Packt Publishing
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First published: September 2015
Production reference: 1240915
Published by Packt Publishing Ltd.
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ISBN 978-1-78328-443-6
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Credits
Author
Ashish Gupta
Reviewers
Siva Prakash
Tharindu Rusira
Commissioning Editor
Akram Hussain
Acquisition Editors
Vivek Anantharaman
Divya Poojari
Content Development Editor
Susmita Sabat
Technical Editor
Namrata Patil
Copy Editor
Merilyn Pereira
Project Coordinator
Judie Jose
Proofreader
Safis Editng
Indexer
Rekha Nair
Graphics
Abhinash Sahu
Production Coordinator
Manu Joseph
Cover Work
Manu Joseph
About the Author
Ashish Gupta has been working in the field of software development for the last 10 years. He has worked in companies such as SAP Labs and Caterpillar as a software developer. While working for a start-up predicting potential customers for new fashion apparels using social media, he developed an interest in the field of machine learning. Since then, he has worked on big data technologies and machine learning for different industries, including retail, finance, insurance, and so on. He is passionate about learning new technologies and sharing that knowledge with others. He is the author of the book, Learning Apache Mahout Classification, Packt Publishing. He has organized many boot camps for Apache Mahout and the Hadoop ecosystem.
First of all, I would like to thank the open source communities for their continuous efforts in developing great software. I would also like to thank the reviewers of this book.
Nothing can be accomplished without the support of family, friends, and loved ones; I would like to thank them, especially my wife and son, for their continuous support while writing this book.
About the Reviewers
Siva Prakash has been working in the field of software development for the last 7 years. He is currently working in CISCO, Bangalore. He has extensive development experience in desktop, mobile, and web-based applications in ERP, telecom, and the digital media industry. He is passionate about learning new technologies and sharing knowledge with others. He has worked on big data technologies for the digital media industry. He loves trekking, traveling, music, reading books, and blogging.
He is available on LinkedIn at https://www.linkedin.com/in/techsivam.
Tharindu Rusira is currently working as a graduate research assistant at the School of Computing, University of Utah while pursuing his doctoral studies in computer science, specializing in compiler technology for performance optimization. He is also passionate about machine learning and its applications in a wide spectrum of real-world problems.
Tharindu is available on LinkedIn at https://www.linkedin.com/in/trusira.
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Preface
With the progress in hardware, our storage capacity has increased now, and because of this, there are many organizations that want to store all types of events for analytical purpose. This is giving birth to a new area of machine learning. The field of machine learning is very complex, and writing those algorithms is not a piece of cake. Apache Mahout provides us with readymade algorithms in the area of machine learning and saves us from the complex task of algorithm implementation.
The intention of this book is to cover clustering algorithms available in Apache Mahout. Whether you have already worked on clustering algorithms using some other tool, or whether you are completely new to this field, this book will help you. So, start reading this book, explore the clustering algorithms in a strong, community-supported, open source, and one of the most popular Apache projects—Apache Mahout.
What this book covers
Chapter 1, Understanding Clustering, explains clustering in general. This chapter will further discuss the different distance matrices and how to calculate them.
Chapter 2, Understanding K-means Clustering, introduces K-means clustering and how Mahout can be used for K-means clustering algorithms.
Chapter 3, Understanding Canopy Clustering, introduces Canopy clustering and its uses in Apache Mahout.
Chapter 4, Understanding the Fuzzy K-means Algorithm Using Mahout, talks about the Fuzzy K-means algorithm and how this algorithm works as a preprocessing step for K-means. We will further discuss how to use Mahout for the Fuzzy K-means algorithm.
Chapter 5, Understanding Model-based Clustering, discusses model-based clustering. This chapter further discusses the topic of modeling using Dirichlet clustering.
Chapter 6, Understanding Streaming K-means, introduces the Streaming K-means algorithm, which is used for streaming data. We will further discuss how Mahout can be used for Streaming K-means.
Chapter 7, Spectral Clustering, introduces spectral clustering and how Mahout has implemented spectral clustering.
Chapter 8, Improving Cluster Quality, covers the steps that should be followed to improve cluster quality once you are ready with your clustering algorithm, in detail. It also discusses what techniques Mahout provides to improve cluster quality.
Chapter 9, Creating a Cluster Model for Production, introduces the techniques that should be followed in a production environment while applying the clustering algorithm.
What you need for this book
To use the examples in this book, you should have the following software installed in your system:
Who this book is for
If you are a data scientist who has some experience with the Hadoop ecosystem and machine learning methods and want to try out clustering on large datasets using Mahout, this book is ideal for you. Knowledge of Java is essential.
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Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "Once done, you can test it by typing the command – mahout and this will show you the same screen as shown in preceding figure."
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Note
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Tip
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Chapter 1. Understanding Clustering
Reduced hardware cost is giving us the opportunity to save a lot of data. We are now generating a lot of data, and this data can generate interesting patterns for various industries, which is why machine learning and data mining enthusiasts are responsible for this data.
