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.