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Mining
of
Massive
Datasets
Anand Rajaraman
Kosmix, Inc.
Jeffrey D. Ullman
Stanford Univ.
Copyright
c
2010 Anand Rajaraman and Jeffrey D. Ullman
ii
Preface
This book e volved from material 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 derived from the Web. Further, the book
takes an algorithmic point o f view: data mining is about applying algorithms
to data, rather than using data to “tra in” a machine-learning engine of some
sort. The principal topics covered are:
1. Distributed file systems and map-reduce as a too l for creating parallel
algorithms that succeed on very large amounts of da ta.
2. Similarity search, including the key techniques of minhashing and locality-
sensitive hashing.
3. Data-strea m processing and specialized algorithms for dealing with data
that arrives so fast it must be processed immediately o r lost.
4. The technology of sear ch 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 as XQuery.
2. A sophomore-level course in data structures, algorithms, and discrete
math.
3. A sophomore-level course in software s ystems, software engineer ing, and
programming language s.
Exercises
The book c ontains extensive e xercises, with some for almost every section. We
indicate harder exercises or parts of exercises with an exclamation point. The
hardest exercises have a double exclamation p oint.
Support on the Web
You can find materials from past offerings 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
We would like to thank Fo to Afrati and Ar un Marathe fo r critical readings of
the draft of this manuscript. Errors were also reported by Shrey Gupta, Mark
Storus, and Roshan Sumbaly. The remaining errors are ours, of course.
A. R.
J. D. U.
Palo Alto, CA
April, 2010
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 o f Bonferroni’s Principle . . . . . . . . . . . 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 . . . . . . . . . . . . . . . . . . . . . . . 18
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 O rganization . . . . . . . . . . . 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 . . . . . . . . . . . . . . . . . . . . . . 24
2.2.4 Combiners . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
v
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