Mining of Massive Datasets
      The book has now been published by Cambridge University Press. The publisher is offering a 20% discount to anyone who buys the hardcopy Here. By agreement with the publisher, you can still download it free from this page. Cambridge Press does, however, retain copyright on the work, and we expect that you will obtain their permission and acknowledge our authorship if you republish parts or all of it. We are sorry to have to mention this point, but we have evidence that other items we have published on the Web have been appropriated and republished under other names. It is easy to detect such misuse, by the way, as you will learn in Chapter 3.
      --- Jure Leskovec, Anand Rajaraman (@anand_raj), and Jeff Ullman

      Contents

      1. The Original Book.
      2. The Latest Version of the Book.
      3. Support Materials, including Gradiance automated homeworks for the book, slides, and the errata sheet.

      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

      Gradiance Support

      If you are an instructor interested in using the Gradiance Automated Homework System with this book, start by creating an account for yourself at www.gradiance.com/services. Then, email your chosen login and the request to become an instructor for the MMDS book to support@gradiance.com You will then be able to create a class using these materials. Manuals explaining the use of the system are at www.gradiance.com/info.html.

      Students who want to use the Gradiance system for self-study can register at www.gradiance.com/services. Then, use the class token 1EDD8A1D to join the "omnibus class" for the MMDS book. See The Student Guide for more information.

      Other Stuff