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Book: Mining of Massive Datasets, 2nd Edition, free download


The second edition of this landmark book adds Jure Leskovec as a coauthor and has 3 new chapters, on mining large graphs, dimensionality reduction, and machine learning. You can still freely download a PDF version.



Mining of Massive Datasets Mining of Massive Datasets ,

by Jure Leskovec @jure,
Anand Rajaraman @anand_raj,
and Jeff Ullman.

The first edition was published by Cambridge University Press, and you get 20% discount by buying it here.

The second edition of the book will also be published soon. Jure Leskovec was added as a coauthor. There are three new chapters, on mining large graphs, dimensionality reduction, and machine learning.

There is a revised Chapter 2 that treats map-reduce programming in a manner closer to how it is used in practice, rather than how it was described in the original paper. Chapter 2 also has new material on algorithm design techniques for map-reduce.

Support Materials include Gradiance automated homeworks for the book and slides.

Thanks to authors agreement with the publisher, you can still download it free from infolab.stanford.edu/~ullman/mmds.html .

The authors note if want to reuse parts of this book, you need to obtain their permission and acknowledge our authorship. They have seen evidence that other items they published have been appropriated and republished under other names, but that is easy to detect, as you will learn in Chapter 3.

Download chapters of the book:

Preface and Table of Contents
Chapter 1 Data Mining
Chapter 2 Map-Reduce and the New Software Stack
Chapter 3 Finding Similar Items
Chapter 4 Mining Data Streams
Chapter 5 Link Analysis
Chapter 6 Frequent Itemsets
Chapter 7 Clustering
Chapter 8 Advertising on the Web
Chapter 9 Recommendation Systems
Chapter 10 Mining Social-Network Graphs
Chapter 11 Dimensionality Reduction
Chapter 12 Large-Scale Machine Learning
Index

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