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Mining of Massive Datasets Hardcover - 2014 - 2nd Edition
by Jure Leskovec; Anand Rajaraman; Jeffrey David Ullman
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- 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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Intl. Ed.
Mining Of Massive Datasets, 2 Ed
by Jure Leskovec, Anand Rajaraman, Jeffrey
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Wiley. Softcover. Brand New. International Edition - ISBN number and front cover may be different in rare cases but CONTENTS are same as the US edition. No shipping to PO BOX, APO, FPO addresses. Kindly provide day time phone number in order to ensure smooth delivery. Printed in black & white in English language. Territorial restrictions may be printed on the book. We may ship from Asian regions for inventory purpose. 100% Customer satisfaction guaranteed!" We use Fast Shipping via DHL/FEDEX/UPS
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Mining of Massive Datasets
by Rajaraman, Anand,Leskovec, Jure,Ullman, Jeffrey David
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- 9781107077232 / 1107077230
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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
by Leskovec, Jure; Rajaraman, Anand; Ullman, Jeffrey David
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- 9781107077232 / 1107077230
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Franklin Lakes, New Jersey, United States
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Cambridge University Press, 2014-11-13. Hardcover. Good. Textbook, May Have Highlights, Notes and/or Underlining, BOOK ONLY-NO ACCESS CODE, NO CD, Ships with Emailed Tracking
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Mining of Massive Datasets by Leskovec, Jure
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- 9781107077232 / 1107077230
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ISBN: 9781107077232Cambridge 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
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