Mining of Massive Datasets

Big-data is transforming the world. Here you will learn data mining and machine learning techniques to process large datasets and extract valuable knowledge from them.

The book

The book is based on Stanford Computer Science course CS246: Mining Massive Datasets (and CS345A: Data Mining).

The book, like the course, is designed at the undergraduate computer science level with no formal prerequisites. To support deeper explorations, most of the chapters are supplemented with further reading references.

The Mining of Massive Datasets book has been published by Cambridge University Press. You can get a 20% discount by applying the code MMDS20 at checkout.

By agreement with the publisher, you can download the book for free from this page. Cambridge University 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 welcome your feedback on the manuscript.

The 3rd edition of the book

The following is the third edition of the book. It contains new material on Spark, Tensorflow, minhashing, community-finding, simrank, graph algorithms, and decision trees. There is a new chapter 13, covering deep learning.

We also offer a set of lecture slides that we use for teaching Stanford CS246: Mining Massive Datasets course. Note that the slides do not necessarily cover all the material convered in the corresponding chapters.

Chapter Title Book Slides Videos Preface and Table of Contents PDF Chapter 1 Data Mining PDF PDF PPT Chapter 2 Map-Reduce and the New Software Stack PDF PDF PPT 1 2 3 4 5 6 7 8 Chapter 3 Finding Similar Items PDF PDF PPT 1 2 3 4 5 6 7 8 9 10 11 12 13 Chapter 4 Mining Data Streams PDF Part 1:

Part 2: PDF

PDF PPT

PPT 1 2 3 4 5 Chapter 5 Link Analysis PDF Part 1:

Part 2: PDF

PDF PPT

PPT 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Chapter 6 Frequent Itemsets PDF PDF PPT 1 2 3 4 Chapter 7 Clustering PDF PDF PPT 1 2 3 4 5 Chapter 8 Advertising on the Web PDF PDF PPT 1 2 3 4 Chapter 9 Recommendation Systems PDF Part 1:

Part 2: PDF

PDF PPT

PPT 1 2 3 4 5 Chapter 10 Mining Social-Network Graphs PDF Part 1:

Part 2: PDF

PDF PPT

PPT 1 2 3 4 5 6 7 8 9 10 11 12 Chapter 11 Dimensionality Reduction PDF PDF PPT 1 2 3 4 5 6 7 8 9 10 11 12 Chapter 12 Large-Scale Machine Learning PDF Part 1:

Part 2: PDF

PDF PPT

PPT 1 2 3 4 5 6 7 8 9 10 11 12 Chapter 13 Neural Nets and Deep Learning PDF Index PDF Errata HTML

Download the latest version of the book as a single big PDF file (603 pages, 3.6 MB).

The Errata for the third edition of the book: HTML.

Download slides (PPT) in French: Chapter 4, Chapter 5, Chapter 8, Chapter 9, Chapter 10. Courtesy of Richard Khoury.

Note to the users of provided slides: We would be delighted if you found this our material useful in giving your own lectures. Feel free to use these slides verbatim, or to modify them to fit your own needs. PowerPoint originals are available. If you make use of a significant portion of these slides in your own lecture, please include this message, or a link to our web site: http://www.mmds.org/.

Comments and corrections are most welcome. Please let us know if you are using these materials in your course and we will list and link to your course.

Stanford big data courses

CS246

CS246: Mining Massive Datasets is graduate level course that discusses data mining and machine learning algorithms for analyzing very large amounts of data. The emphasis is on Map Reduce as a tool for creating parallel algorithms that can process very large amounts of data.

CS341

CS341 Project in Mining Massive Data Sets is an advanced project based course. Students work on data mining and machine learning algorithms for analyzing very large amounts of data. Both interesting big datasets as well as computational infrastructure (large MapReduce cluster) are provided by course staff. Generally, students first take CS246 followed by CS341.

CS341 is generously supported by Amazon by giving us access to their EC2 platform.

CS224W

CS224W: Social and Information Networks is graduate level course that covers recent research on the structure and analysis of such large social and information networks and on models and algorithms that abstract their basic properties. Class explores how to practically analyze large scale network data and how to reason about it through models for network structure and evolution.

You can take Stanford courses!

If you are not a Stanford student, you can still take CS246 as well as CS224W or earn a Stanford Mining Massive Datasets graduate certificate by completing a sequence of four Stanford Computer Science courses. A graduate certificate is a great way to keep the skills and knowledge in your field current. More information is available at the Stanford Center for Professional Development (SCPD).

Supporting materials

If you are an instructor interested in using the Gradiance Automated Homework System with this book, start by creating an account for yourself here. 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 available here.

Students who want to use the Gradiance Automated Homework System for self-study can register here. Then, use the class token 1EDD8A1D to join the "omnibus class" for the MMDS book. See The Student Guide for more information.

Previous versions of the book

The 2nd edition of the book (v2.1)

The following is the second edition of the book. There are three new chapters, on mining large graphs, dimensionality reduction, and machine learning. There is also a revised Chapter 2 that treats map-reduce programming in a manner closer to how it is used in practice.

Together with each chapter there is aslo a set of lecture slides that we use for teaching Stanford CS246: Mining Massive Datasets course. Note that the slides do not necessarily cover all the material convered in the corresponding chapters.

Chapter Title Book Slides Videos Preface and Table of Contents PDF Chapter 1 Data Mining PDF PDF PPT Chapter 2 Map-Reduce and the New Software Stack PDF PDF PPT 1 2 3 4 5 6 7 8 Chapter 3 Finding Similar Items PDF PDF PPT 1 2 3 4 5 6 7 8 9 10 11 12 13 Chapter 4 Mining Data Streams PDF Part 1:

Part 2: PDF

PDF PPT

PPT 1 2 3 4 5 Chapter 5 Link Analysis PDF Part 1:

Part 2: PDF

PDF PPT

PPT 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Chapter 6 Frequent Itemsets PDF PDF PPT 1 2 3 4 Chapter 7 Clustering PDF PDF PPT 1 2 3 4 5 Chapter 8 Advertising on the Web PDF PDF PPT 1 2 3 4 Chapter 9 Recommendation Systems PDF Part 1:

Part 2: PDF

PDF PPT

PPT 1 2 3 4 5 Chapter 10 Mining Social-Network Graphs PDF Part 1:

Part 2: PDF

PDF PPT

PPT 1 2 3 4 5 6 7 8 9 10 11 12 Chapter 11 Dimensionality Reduction PDF PDF PPT 1 2 3 4 5 6 7 8 9 10 11 12 Chapter 12 Large-Scale Machine Learning PDF Part 1:

Part 2: PDF

PDF PPT

PPT 1 2 3 4 5 6 7 8 9 10 11 12 Index PDF Errata HTML

Download the latest version of the book as a single big PDF file (511 pages, 3 MB).

Download the full version of the book with a hyper-linked table of contents that make it easy to jump around: PDF file (513 pages, 3.69 MB).

The Errata for the second edition of the book: HTML.

Download slides (PPT) in French: Chapter 4, Chapter 5, Chapter 8, Chapter 9, Chapter 10. Courtesy of Richard Khoury.

Note to the users of provided slides: We would be delighted if you found this our material useful in giving your own lectures. Feel free to use these slides verbatim, or to modify them to fit your own needs. PowerPoint originals are available. If you make use of a significant portion of these slides in your own lecture, please include this message, or a link to our web site: http://www.mmds.org/.

Version 1.0

The following materials are equivalent to the published book, with errata corrected to July 4, 2012.

Chapter Title Book Preface and Table of Contents PDF Chapter 1 Data Mining PDF Chapter 2 Large-Scale File Systems and Map-Reduce PDF Chapter 3 Finding Similar Items PDF Chapter 4 Mining Data Streams PDF Chapter 5 Link Analysis PDF Chapter 6 Frequent Itemsets PDF Chapter 7 Clustering PDF Chapter 8 Advertising on the Web PDF Chapter 9 Recommendation Systems PDF Index PDF Errata HTML

Download the book as published here (340 pages, 2 MB).