• Title Mining of Massive Datasets
• Author(s) Jure Leskovec, Anand Rajaraman, Jeffrey D. Ullman
• Publisher: Cambridge University Press; 3rd edition (February 13, 2020); eBook (Online Edition)
• Permission: By agreement with the publisher, you can download the book for free from the book's homepage.
• Hardcover 565 pages
• eBook PDF files
• Language: English
• ISBN-10: 1108476341
• ISBN-13: 978-1108476348
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Book Description

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.

• Jure Leskovec is Assistant Professor of Computer Science at Stanford University. His research focuses on mining large social and information networks.
• Anand Rajaraman is a serial entrepreneur, venture capitalist, and academic based in Silicon Valley. He is a Founding Partner of two early-stage venture capital firms, Milliways Labs and Cambrian Ventures.
• Jeffrey David Ullman is the Stanford W. Ascherman Professor of Computer Science (Emeritus) and he is currently the CEO of Gradiance. His research interests include database theory, data mining, and education using the information infrastructure. He is one of the founders of the field of database theory, and was the doctoral advisor of an entire generation of students who later became leading database theorists in their own right.

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• Data Analysis and Data Mining
• Big Data
• Data Science
• Machine Learning
• Algorithms and Data Structures

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