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Mining of Massive Datasets

$69.00

textbook
  • Date Published: December 2011
  • availability: In stock
  • format: Hardback
  • isbn: 9781107015357

$69.00
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About the Authors
  • The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and which can be used on even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. The PageRank idea and related tricks for organizing the Web are covered next. Other chapters cover the problems of finding frequent itemsets and clustering. The final chapters cover two applications: recommendation systems and Web advertising, each vital in e-commerce. Written by two authorities in database and Web technologies, this book is essential reading for students and practitioners alike.

    • Teaching material has been 'road-tested' for several years at Stanford University
    • Includes a range of exercises to challenge even the most able student
    • Resources for instructors are available online, including slides, homework assignments, project requirements and exams
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    Product details

    • Date Published: December 2011
    • format: Hardback
    • isbn: 9781107015357
    • length: 326 pages
    • dimensions: 253 x 178 x 23 mm
    • weight: 0.8kg
    • contains: 90 b/w illus. 160 exercises
    • availability: In stock
  • Table of Contents

    1. Data mining
    2. Large-scale file systems and map-reduce
    3. Finding similar items
    4. Mining data streams
    5. Link analysis
    6. Frequent itemsets
    7. Clustering
    8. Advertising on the Web
    9. Recommendation systems
    Index.

  • general resources

    View all resources
    Group Section Name Type Size Sort Order filter vars
    General ResourcesView materials from the authors' "Web Mining" course, including slides, homework assignments and examslinkn/aSort Order general resources general resources general resources general resources
    General ResourcesView errata for this booklinkn/aSort Order general resources general resources general resources general resources

    These resources are provided free of charge by Cambridge University Press with permission of the author of the corresponding work, but are subject to copyright. You are permitted to view, print and download these resources for your own personal use only, provided any copyright lines on the resources are not removed or altered in any way. Any other use, including but not limited to distribution of the resources in modified form, or via electronic or other media, is strictly prohibited unless you have permission from the author of the corresponding work and provided you give appropriate acknowledgement of the source.

    If you are having problems accessing these resources please email cflack@cambridge.org

  • Authors

    Anand Rajaraman, WalmartLabs

    Jeffrey David Ullman, Stanford University, California

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