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Publication Date: December 30, 2011 | ISBN-10: 1107015359 | ISBN-13: 978-1107015357

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 which can be used on 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. The PageRank idea and related tricks for organizing the Web are covered next. Other chapters cover the problems of finding frequent itemsets and clustering. The final chapters cover two applications: recommendation systems and Web advertising, each vital in e-commerce. Written by two authorities in database and Web technologies, this book is essential reading for students and practitioners alike.

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.

--- 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 1.2

Below is a draft, evolving version of the MMDS book. We have added Jure Leskovec as a coauthor, and at this point added only one new chapter, on mining large graphs. However, we will be making available new chapters on large-scale machine-learning algorithms and dimensionality reduction.

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.

Download the Latest Book (415 pages, approximately 2.5MB)

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 
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 1.2 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 [email protected] You will then be able to create a class using these materials. Manuals explaining the use of the system are atwww.gradiance.com/info.html.

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Comment by Juan Carlos Borras on February 26, 2013 at 6:35am

It's got some math in it as it can't be otherwise but it is a very approachable book even by non-technical personnel.

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