Mining of Massive Datasets

Nonfiction, Computers, Database Management, General Computing
Cover of the book Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman, Cambridge University Press
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Author: Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman ISBN: 9781316147047
Publisher: Cambridge University Press Publication: November 13, 2014
Imprint: Cambridge University Press Language: English
Author: Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman
ISBN: 9781316147047
Publisher: Cambridge University Press
Publication: November 13, 2014
Imprint: Cambridge University Press
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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