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This book evolved from material developed over several years by Anand Rajaraman and Jeff Ullman for a one-quarter course at Stanford. The course CS345A, titled “Web Mining,” was designed as an advanced graduate course,although it has become accessible and interesting to advanced undergraduates.
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Mining
of
Massive
Datasets
Anand Rajaraman
Kosmix, Inc.
Jeffrey D. Ullman
Stanford Univ.
Copyright
c
2010, 2011 Anand Rajaraman and Jeffrey D. Ullman
ii
Preface
This book evolved from ma terial developed over several years by Anand Raja-
raman and Jeff Ullman for a one-quarter course at Stanford. The course
CS345A, titled “Web Mining,” was designed as an advanced graduate course,
although it has become accessible and interesting to advanced undergraduates.
What the Book Is About
At the highest level of description, this book is about data mining. However,
it focuses on data mining of very large amounts of data, that is, data so large
it does not fit in main memory. Because of the emphasis on size, many of our
examples are about the Web or data der ived from the Web. Further, the bo ok
takes an algorithmic point o f view: data mining is about applying algorithms
to data, rather than using data to “train” a machine-learning eng ine of some
sort. The principal topics covered are:
1. Distributed file s ystems and map-reduce as a tool for creating parallel
algorithms that succeed on very large amounts of data.
2. Similarity search, including the key techniques of minhashing and locality-
sensitive hashing.
3. Data-stream processing and specialized a lgorithms for dealing with data
that arrives so fast it must be processed immediately or lost.
4. The technology of search engines, including Google’s PageRank, link-spam
detection, and the hubs-and-authorities approach.
5. Frequent-itemset mining, including association rules, market-baskets, the
A-Priori Algor ithm and its improvements.
6. Algorithms for clustering very large, high-dimensional datasets.
7. Two key problems for Web applica tions: managing advertising and rec-
ommendation systems.
iii
iv PREFACE
Prerequisites
CS345A, although its number indicates an advanced graduate course, has been
found accessible by advanced underg raduates and beginning masters students.
In the future, it is likely that the course will be given a mezzanine-level number.
The prerequisites for CS345A are:
1. The first course in databa se systems, covering application programming
in SQL and other database-related languages such a s XQuery.
2. A sophomore-level course in data structures, algorithms, and discre te
math.
3. A sophomore-level course in software systems, software engineering, and
programming languages.
Exercises
The book contains extensive exercises, with some for almost every section. We
indicate harder exercises or parts of exercis e s with an exclamation point. The
hardest exercises have a double exclamation point.
Support on the Web
You can find materials from past o fferings of CS345A at:
http://infolab.stanford.edu/~ullman/mining/mining.html
There, you will find slides, homework assignments, project requirements, and
in some cases, exams.
Acknowledgements
Cover art is by Scott Ullman. We would like to thank Foto Afrati and Arun
Marathe for critical readings of the draft of this manuscript. Errors were also
reported by Leland Chen, Shrey Gupta, Xie Ke, Haewoon Kwak, Brad Penoff,
Philips Kokoh Prasetyo, Mark Storus, Tim Triche Jr., and Roshan Sumbaly.
The remaining errors are ours, of course.
A. R.
J. D. U.
Palo Alto, CA
June, 2011
Contents
1 Data Mining 1
1.1 What is Data Mining? . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.1 Statistical Modeling . . . . . . . . . . . . . . . . . . . . . 1
1.1.2 Machine Learning . . . . . . . . . . . . . . . . . . . . . . 2
1.1.3 Computational Approaches to Modeling . . . . . . . . . . 2
1.1.4 Summarization . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1.5 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . 4
1.2 Statistical Limits on Data Mining . . . . . . . . . . . . . . . . . . 4
1.2.1 Total Information Awareness . . . . . . . . . . . . . . . . 5
1.2.2 Bonferroni’s Principle . . . . . . . . . . . . . . . . . . . . 5
1.2.3 An Example of Bonferr oni’s Pr inciple . . . . . . . . . . . 6
1.2.4 Exercises fo r Section 1.2 . . . . . . . . . . . . . . . . . . . 7
1.3 Things Useful to Know . . . . . . . . . . . . . . . . . . . . . . . . 7
1.3.1 Importance of Words in Documents . . . . . . . . . . . . 7
1.3.2 Hash Functions . . . . . . . . . . . . . . . . . . . . . . . . 9
1.3.3 Indexes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.3.4 Secondary Stor age . . . . . . . . . . . . . . . . . . . . . . 11
1.3.5 The Base of Natura l Log arithms . . . . . . . . . . . . . . 12
1.3.6 Power Laws . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.3.7 Exercises fo r Section 1.3 . . . . . . . . . . . . . . . . . . . 15
1.4 Outline of the Book . . . . . . . . . . . . . . . . . . . . . . . . . 15
1.5 Summary of Chapter 1 . . . . . . . . . . . . . . . . . . . . . . . . 17
1.6 References for Chapter 1 . . . . . . . . . . . . . . . . . . . . . . . 17
2 Large-Scale File Systems and Map-Reduce 19
2.1 Distributed File Systems . . . . . . . . . . . . . . . . . . . . . . . 20
2.1.1 Physical Organization of Compute Nodes . . . . . . . . . 20
2.1.2 Large-Scale File-System Or ganization . . . . . . . . . . . 21
2.2 Map-Reduce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.2.1 The Map Tasks . . . . . . . . . . . . . . . . . . . . . . . . 23
2.2.2 Grouping and Aggregation . . . . . . . . . . . . . . . . . 24
2.2.3 The Reduce Tasks . . . . . . . . . . . . . . . . . . . . . . 2 4
2.2.4 Combiners . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
v
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