Mining of Massive Datasets
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- Jure Leskovec, Stanford University, California
- Anand Rajaraman, Milliways Laboratories, California
- Jeffrey David Ullman, Stanford University, California
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Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike. The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets and clustering. This second edition includes new and extended coverage on social networks, machine learning and dimensionality reduction.Read more
- Contains brand new material and extended coverage of important topics
- Includes a range of over 150 exercises to challenge even the most able student
- Slides, homework assignments, project requirements and exams are available from http://infolab.stanford.edu/~ullman/mining/mining.html
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- Edition: 2nd Edition
- Date Published: November 2014
- format: Adobe eBook Reader
- isbn: 9781316147313
- contains: 150 b/w illus. 210 exercises
- availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
Table of Contents
1. Data mining
2. Map-reduce and the new software stack
3. Finding similar items
4. Mining data streams
5. Link analysis
6. Frequent itemsets
8. Advertising on the Web
9. Recommendation systems
10. Mining social-network graphs
11. Dimensionality reduction
12. Large-scale machine learning
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