Mining of Massive Datasets

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.

Author: Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman
Publisher: Cambridge University Press
Published: 01/09/2020
Pages: 565
Binding Type: Hardcover
Weight: 2.70lbs
Size: 9.80h x 7.20w x 1.10d
ISBN13: 9781108476348
ISBN10: 1108476341
BISAC Categories:
- Computers | Artificial Intelligence | Computer Vision & Pattern Recognit