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  Mahout in Action (Paperback) By (author) Sean Owen, By (author) Robin Anil, By (author) Ted Dunning, By (author) Ellen Friedman

  

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  Short Description for Mahout in ActionThe Apache Mahout project is focused on three types of machine learning: recommendation systems, classification, and clustering. This text introduces the sorts of problems that these techniques are appropriate for, and then illustrates how Mahout can be applied to solve them.
  Full description

  • Publisher: Manning Publications
  • Published: 28 October 2011
  • Format: Paperback 375 pages
  • See: Full bibliographic data
  • Categories: Algorithms & Data Structures | Web Programming | Programming & Scripting Languages: General
  • ISBN 13: 9781935182689 ISBN 10: 1935182684
  • Sales rank: 116,335

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Full description for Mahout in Action

• Summary"Mahout in Action" is a hands-on introduction to machine learning with Apache Mahout. Following real-world examples, the book presents practical use cases and then illustrates how Mahout can be applied to solve them. Includes a free audio- and video-enhanced ebook.About the TechnologyA computer system that learns and adapts as it collects data can be really powerful. Mahout, Apache's open source machine learning project, captures the core algorithms of recommendation systems, classification, and clustering in ready-to-use, scalable libraries. With Mahout, you can immediately apply to your own projects the machine learning techniques that drive Amazon, Netflix, and others.About this BookThis book covers machine learning using Apache Mahout. Based on experience with real-world applications, it introduces practical use cases and illustrates how Mahout can be applied to solve them. It places particular focus on issues of scalability and how to apply these techniques against large data sets using the Apache Hadoop framework.This book is written for developers familiar with Java -- no prior experience with Mahout is assumed.What's InsideUse group data to make individual recommendationsFind logical clusters within your dataFilter and refine with on-the-fly classificationFree audio and video extrasTable of ContentsMeet Apache MahoutPART 1 RECOMMENDATIONSIntroducing recommendersRepresenting recommender dataMaking recommendationsTaking recommenders to productionDistributing recommendation computationsPART 2 CLUSTERINGIntroduction to clusteringRepresenting dataClustering algorithms in MahoutEvaluating and improving clustering qualityTaking clustering to productionReal-world applications of clusteringPART 3 CLASSIFICATIONIntroduction to classificationTraining a classifier Evaluating and tuning a classifierDeploying a classifierCase study: Shop It To Me