Description: Mining of Massive Datasets Please note: this item is printed on demand and will take extra time before it can be dispatched to you (up to 20 working days). Author(s): Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman Format: Hardback Publisher: Cambridge University Press, United Kingdom Imprint: Cambridge University Press ISBN-13: 9781108476348, 978-1108476348 Synopsis 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.
Price: 58.78 GBP
Location: Aldershot
End Time: 2025-02-10T09:34:55.000Z
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Book Title: Mining of Massive Datasets
Number of Pages: 565 Pages
Publication Name: Mining of Massive Datasets
Language: English
Publisher: Cambridge University Press
Item Height: 253 mm
Subject: Computer Science, Management
Publication Year: 2020
Type: Textbook
Item Weight: 1240 g
Subject Area: Information Science
Author: Anand Rajaraman, Jure Leskovec, Jeffrey David Ullman
Item Width: 178 mm
Format: Hardcover
