Mining of Massive Datasets (Hardcover, 3rd Revised edition)

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 MapReduce 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 third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs.

General

Imprint:	Cambridge UniversityPress
Country of origin:	United Kingdom
Release date:	2020
Authors:	Jure Leskovec • Anand Rajaraman • Jeffrey David Ullman
Dimensions:	253 x 178 x 32mm (L x W x T)
Format:	Hardcover
Pages:	565
Edition:	3rd Revised edition
ISBN-13:	978-1-108-47634-8
Categories:	Books > Computing & IT > Applications of computing > Pattern recognition Books > Reference & Interdisciplinary > Communication studies > Information theory > General Books > Business & Economics > Business & management > Management of specific areas > General Books > Computing & IT > Applications of computing > Databases > Data mining Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	1-108-47634-1
Barcode:	9781108476348

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