Mining of Massive Datasets (Hardcover, New)

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.

General

Imprint:	Cambridge UniversityPress
Country of origin:	United Kingdom
Release date:	October 2011
First published:	October 2011
Authors:	Anand Rajaraman • Jeffrey David Ullman
Dimensions:	246 x 189 x 23mm (L x W x T)
Format:	Hardcover
Pages:	326
Edition:	New
ISBN-13:	978-1-107-01535-7
Categories:	Books > Computing & IT > Applications of computing > Databases > Data mining Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Promotions
LSN:	1-107-01535-9
Barcode:	9781107015357

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