Other available formats:
Looking for an examination copy?
If you are interested in the title for your course we can consider offering an examination copy. To register your interest please contact firstname.lastname@example.org providing details of the course you are teaching.
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
Not yet reviewed
Be the first to review
Review was not posted due to profanity×
- Edition: 2nd Edition
- Date Published: December 2014
- format: Hardback
- isbn: 9781107077232
- length: 476 pages
- dimensions: 253 x 180 x 30 mm
- weight: 0.99kg
- contains: 150 b/w illus. 210 exercises
- availability: In stock
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
Welcome to the resources site
Here you will find free-of-charge online materials to accompany this book. The range of materials we provide across our academic and higher education titles are an integral part of the book package whether you are a student, instructor, researcher or professional.
Find resources associated with this titleYour search for '' returned .
Type Name Unlocked * Format Size
*This title has one or more locked files and access is given only to instructors adopting the textbook for their class. We need to enforce this strictly so that solutions are not made available to students. To gain access to locked resources you either need first to sign in or register for an account.
These resources are provided free of charge by Cambridge University Press with permission of the author of the corresponding work, but are subject to copyright. You are permitted to view, print and download these resources for your own personal use only, provided any copyright lines on the resources are not removed or altered in any way. Any other use, including but not limited to distribution of the resources in modified form, or via electronic or other media, is strictly prohibited unless you have permission from the author of the corresponding work and provided you give appropriate acknowledgement of the source.
If you are having problems accessing these resources please email email@example.com
Sorry, this resource is locked
Please register or sign in to request access. If you are having problems accessing these resources please email firstname.lastname@example.orgRegister Sign in
You are now leaving the Cambridge University Press website. Your eBook purchase and download will be completed by our partner www.ebooks.com. Please see the permission section of the www.ebooks.com catalogue page for details of the print & copy limits on our eBooks.Continue ×