Mining of Massive Datasets

Posted on 2011.09.19 by Jose Ibarra

via Stanford University InfoLab

This book is placed on the Web for free use of all who wish it. We do, however, retain copyright on the work, and we expect that you will 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.

Download the book Stanford Data Mining – Data Sets

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

  • Slides and Course Material from old CS345A. Like the book, you are welcome to use these as you like, but please preserve our authorship.
  • The Errata Sheet. We shall endeavor to keep the downloads up to date. But if you bought or printed out a copy, you can check this list for known errors with the date of discovery. Please report errata to ullman a t gmail.com.

Original Post Stanford University InfoLab

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