Mining of Massive Datasets~GeneralIssues~.pdf

Mining of Massive Datasets

Anand Rajaraman
Jure Leskovec
Stanford Univ.
Jeffrey D. Ullman
Stanford Univ.

Preface
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.
When Jure Leskovec joined the Stanford faculty, we reorganized the material
considerably. He introduced a new course CS224W on network analysis and
added material to CS345A, which was renumbered CS246. The three authors
also introduced a large-scale data-mining project course, CS341. The book now
contains material taught in all three courses.

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 of view: data mining is about applying algorithms
to data, rather than using data to “train” a machine-learning engine of some
sort. The principal topics covered are:

1. Distributed file systems 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 localitysensitive hashing.
3. Data-stream processing and specialized algorithms 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 Algorithm and its improvements.
6. Algorithms for clustering very large, high-dimensional datasets.
iiiiv PREFACE
7. Two key problems for Web applications: managing advertising and recommendation systems.
8. Algorithms for analyzing and mining the structure of very large graphs,
especially social-network graphs.
Prerequisites
To appreciate fully the material in this book, we recommend the following
prerequisites:
1. An introduction to database systems, covering SQL and related programming systems.
2. A sophomore-level course in data structures, algorithms, and discrete
math.
3. A sophomore-level course in software systems, software engineering, and
programming languages.

-GeneralIssues