Mining of Massive Datasets

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--- Jure Leskovec, Anand Rajaraman (@anand_raj), and Jeff Ullman

Contents

Download Version 2.1

The following is the second edition of the book, which we expect to be published soon. We have added Jure Leskovec 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.

Version 2.1 adds Section 10.5 on finding overlapping communities in social graphs.

Download the Latest Book (511 pages, approximately 3MB)

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

Download Version 1.0

The following materials are equivalent to the published book, with errata corrected to July 4, 2012. It has been frozen as we revise the book. The evolving book can be downloaded as Version 1.3 above.

Download the Book as Published (340 pages, approximately 2MB)

Download chapters of the book:

Preface and Table of Contents

Chapter 1 Data Mining

Chapter 2 Large-Scale File Systems and Map-Reduce

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

Index

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