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Mining of Massive Datasets
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Mining of Massive Datasets Hardcover - 2014 - 2nd Edition

by Jure Leskovec; Anand Rajaraman; Jeffrey David Ullman


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Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike. 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 can be applied successfully to 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. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets and clustering. This second edition includes new and extended coverage on social networks, machine learning and dimensionality reduction.

Details

  • Title Mining of Massive Datasets
  • Author Jure Leskovec; Anand Rajaraman; Jeffrey David Ullman
  • Binding Hardcover
  • Edition number 2nd
  • Edition 2
  • Pages 476
  • Volumes 1
  • Language ENG
  • Publisher Cambridge University Press
  • Date 2014-12
  • ISBN 9781107077232 / 1107077230
  • Weight 2.18 lbs (0.99 kg)
  • Dimensions 9.96 x 7.09 x 1.18 in (25.30 x 18.01 x 3.00 cm)
  • Dewey Decimal Code 006.312
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Mining Of Massive Datasets, 2 Ed

by Jure Leskovec, Anand Rajaraman, Jeffrey

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Mining of Massive Datasets
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Mining of Massive Datasets

by Rajaraman, Anand,Leskovec, Jure,Ullman, Jeffrey David

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Cambridge University Press, 13/11/2014 00:00:01. hardcover. Like New. 2.9977 in x 25.0809 in x 17.7865 in.
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Mining of Massive Datasets
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Mining of Massive Datasets

by Leskovec, Jure; Rajaraman, Anand; Ullman, Jeffrey David

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Mining of Massive Datasets by Leskovec, Jure

Mining of Massive Datasets by Leskovec, Jure

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ISBN: 9781107077232
Cambridge University Press, 29 December 2014
Hardcover, 476 pages
Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike. 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 can be applied successfully to 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. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets and clustering. This second edition includes new and extended coverage on social networks,… Read More
Item Price
$45.05
$31.80 shipping to USA