Preface
This book evolved 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.
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, w hich was renumbe red 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 a bout 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. Be c ause 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 locality-
sensitive hashing.
3. Data-stream processing and specialized algorithms for dealing with data
that a rrives 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-a uthorities appro ach.
5. Frequent-itemset mining, including association rules, market-baskets, the
A-Priori Algorithm and its improvements.
6. Algorithms for clustering very large, high-dimensiona l datasets.
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