Apache Mahout Cookbook.

By: Giacomelli, PieroPublisher: Olton : Packt Publishing, Limited, 2013Copyright date: ©2013Description: 1 online resource (276 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9781849518031Subject(s): Documentary films -- Production and direction.;Documentary films -- AuthorshipGenre/Form: Electronic books. Additional physical formats: Print version:: Apache Mahout CookbookDDC classification: 006.31 LOC classification: Q325.5 -- .G53 2013ebOnline resources: Click to View
Contents:
Intro -- Apache Mahout Cookbook -- Table of Contents -- Apache Mahout Cookbook -- Credits -- About the Author -- Acknowledgments -- About the Reviewers -- www.PacktPub.com -- Support files, eBooks, discount offers and more -- Why subscribe? -- Free Access for Packt account holders -- Preface -- What this book covers -- What you need for this book -- Who this book is for -- Conventions -- Reader feedback -- Customer support -- Downloading the example code -- Errata -- Piracy -- Questions -- 1. Mahout is Not So Difficult! -- Introduction -- Installing Java and Hadoop -- Getting ready -- How to do it... -- Setting up a Maven and NetBeans development environment -- Getting ready -- How to do it... -- How it works... -- There's more... -- Coding a basic recommender -- Getting ready -- How to do it... -- How it works... -- See also -- 2. Using Sequence Files - When and Why? -- Introduction -- Creating sequence files from the command line -- Getting ready -- How to do it... -- How it works... -- Generating sequence files from code -- Getting ready -- How to do it... -- How it works... -- Reading sequence files from code -- Getting ready -- How to do it… -- How it works… -- 3. Integrating Mahout with an External Datasource -- Introduction -- Importing an external datasource into HDFS -- Getting ready -- How to do it... -- How it works... -- There's more... -- Exporting data from HDFS to RDBMS -- How to do it… -- How it works... -- Creating a Sqoop job to deal with RDBMS -- How to do it... -- How it works... -- There's more... -- Importing data using Sqoop API -- Getting ready -- How to do it… -- How it works... -- 4. Implementing the Naϊve Bayes classifier in Mahout -- Introduction -- Using the Mahout text classifier to demonstrate the basic use case -- Getting ready -- How to do it… -- How it works... -- There's more.
Using the Naïve Bayes classifier from code -- Getting ready -- How to do it… -- How it works... -- There's more -- Using Complementary Naïve Bayes from the command line -- Getting ready -- How to do it… -- How it works… -- See also -- Coding the Complementary Naïve Bayes classifier -- Getting ready -- How to do it… -- How it works... -- 5. Stock Market Forecasting with Mahout -- Introduction -- Preparing data for logistic regression -- Getting ready -- How to do it… -- How it works… -- Predicting GOOG movements using logistic regression -- Getting ready -- How to do it… -- How it works… -- The confusion matrix -- Using adaptive logistic regression in Java code -- Getting ready -- How to do it… -- How it works… -- Using logistic regression on large-scale datasets -- Getting ready -- How to do it… -- How it works... -- See also -- Using Random Forest to forecast market movements -- Getting ready -- How to do it… -- How it works… -- See also -- 6. Canopy Clustering in Mahout -- Introduction -- Command-line-based Canopy clustering -- Getting ready -- How to do it… -- How it works... -- Command-line-based Canopy clustering with parameters -- Getting ready -- How to do it… -- How it works... -- Using Canopy clustering from the Java code -- Getting ready -- How to do it… -- How it works... -- Coding your own cluster distance evaluation -- Getting ready -- How to do it… -- How it works... -- See also -- 7. Spectral Clustering in Mahout -- Introduction -- Using EigenCuts from the command line -- Getting ready -- How to do it… -- Using EigenCuts from Java code -- Getting ready -- How to do it… -- How it works… -- Creating a similarity matrix from raw data -- Getting ready -- How to do it… -- How it works… -- Using spectral clustering with image segmentation -- Getting ready -- How to do it… -- How it works -- 8. K-means Clustering -- Introduction.
Using K-means clustering from Java code -- Getting started -- How to do it… -- How it works… -- Clustering traffic accidents using K-means -- Getting ready -- How to do it… -- How it works… -- See also -- K-means clustering using MapReduce -- Getting ready -- How to do it… -- How it works… -- Using K-means clustering from the command line -- Getting ready -- How to do it… -- How it works… -- See also -- 9. Soft Computing with Mahout -- Introduction -- Frequent Pattern Mining with Mahout -- Getting ready -- How to do it… -- How it works… -- Creating metrics for Frequent Pattern Mining -- Getting ready -- How to do it… -- How it works… -- Using Frequent Pattern Mining from Java code -- Getting ready -- How to do it… -- Using LDA for creating topics -- Getting ready -- How to do it… -- How it works... -- 10. Implementing the Genetic Algorithm in Mahout -- Introduction -- Setting up Mahout for using GA -- Getting ready -- How to do it… -- Using the genetic algorithm over graphs -- Getting ready -- How to do it… -- How it works... -- Using the genetic algorithm from Java code -- Getting ready -- How to do it… -- How it works... -- There's more... -- Index.
Summary: 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."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.
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Intro -- Apache Mahout Cookbook -- Table of Contents -- Apache Mahout Cookbook -- Credits -- About the Author -- Acknowledgments -- About the Reviewers -- www.PacktPub.com -- Support files, eBooks, discount offers and more -- Why subscribe? -- Free Access for Packt account holders -- Preface -- What this book covers -- What you need for this book -- Who this book is for -- Conventions -- Reader feedback -- Customer support -- Downloading the example code -- Errata -- Piracy -- Questions -- 1. Mahout is Not So Difficult! -- Introduction -- Installing Java and Hadoop -- Getting ready -- How to do it... -- Setting up a Maven and NetBeans development environment -- Getting ready -- How to do it... -- How it works... -- There's more... -- Coding a basic recommender -- Getting ready -- How to do it... -- How it works... -- See also -- 2. Using Sequence Files - When and Why? -- Introduction -- Creating sequence files from the command line -- Getting ready -- How to do it... -- How it works... -- Generating sequence files from code -- Getting ready -- How to do it... -- How it works... -- Reading sequence files from code -- Getting ready -- How to do it… -- How it works… -- 3. Integrating Mahout with an External Datasource -- Introduction -- Importing an external datasource into HDFS -- Getting ready -- How to do it... -- How it works... -- There's more... -- Exporting data from HDFS to RDBMS -- How to do it… -- How it works... -- Creating a Sqoop job to deal with RDBMS -- How to do it... -- How it works... -- There's more... -- Importing data using Sqoop API -- Getting ready -- How to do it… -- How it works... -- 4. Implementing the Naϊve Bayes classifier in Mahout -- Introduction -- Using the Mahout text classifier to demonstrate the basic use case -- Getting ready -- How to do it… -- How it works... -- There's more.

Using the Naïve Bayes classifier from code -- Getting ready -- How to do it… -- How it works... -- There's more -- Using Complementary Naïve Bayes from the command line -- Getting ready -- How to do it… -- How it works… -- See also -- Coding the Complementary Naïve Bayes classifier -- Getting ready -- How to do it… -- How it works... -- 5. Stock Market Forecasting with Mahout -- Introduction -- Preparing data for logistic regression -- Getting ready -- How to do it… -- How it works… -- Predicting GOOG movements using logistic regression -- Getting ready -- How to do it… -- How it works… -- The confusion matrix -- Using adaptive logistic regression in Java code -- Getting ready -- How to do it… -- How it works… -- Using logistic regression on large-scale datasets -- Getting ready -- How to do it… -- How it works... -- See also -- Using Random Forest to forecast market movements -- Getting ready -- How to do it… -- How it works… -- See also -- 6. Canopy Clustering in Mahout -- Introduction -- Command-line-based Canopy clustering -- Getting ready -- How to do it… -- How it works... -- Command-line-based Canopy clustering with parameters -- Getting ready -- How to do it… -- How it works... -- Using Canopy clustering from the Java code -- Getting ready -- How to do it… -- How it works... -- Coding your own cluster distance evaluation -- Getting ready -- How to do it… -- How it works... -- See also -- 7. Spectral Clustering in Mahout -- Introduction -- Using EigenCuts from the command line -- Getting ready -- How to do it… -- Using EigenCuts from Java code -- Getting ready -- How to do it… -- How it works… -- Creating a similarity matrix from raw data -- Getting ready -- How to do it… -- How it works… -- Using spectral clustering with image segmentation -- Getting ready -- How to do it… -- How it works -- 8. K-means Clustering -- Introduction.

Using K-means clustering from Java code -- Getting started -- How to do it… -- How it works… -- Clustering traffic accidents using K-means -- Getting ready -- How to do it… -- How it works… -- See also -- K-means clustering using MapReduce -- Getting ready -- How to do it… -- How it works… -- Using K-means clustering from the command line -- Getting ready -- How to do it… -- How it works… -- See also -- 9. Soft Computing with Mahout -- Introduction -- Frequent Pattern Mining with Mahout -- Getting ready -- How to do it… -- How it works… -- Creating metrics for Frequent Pattern Mining -- Getting ready -- How to do it… -- How it works… -- Using Frequent Pattern Mining from Java code -- Getting ready -- How to do it… -- Using LDA for creating topics -- Getting ready -- How to do it… -- How it works... -- 10. Implementing the Genetic Algorithm in Mahout -- Introduction -- Setting up Mahout for using GA -- Getting ready -- How to do it… -- Using the genetic algorithm over graphs -- Getting ready -- How to do it… -- How it works... -- Using the genetic algorithm from Java code -- Getting ready -- How to do it… -- How it works... -- There's more... -- Index.

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."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.

Description based on publisher supplied metadata and other sources.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2019. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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