Mining of massive datasets
The popularity of the Web and Internet commerce provides many extremely large datasets from which infomration can be gleaned by data mining. This book focuses on practical algorithms that have been used to slve key problems in data mining and can be used on even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The tricks of locality-sensitive hashing are explained. This body of knowledge, which deserves …
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- Full title :Mining of massive datasets / Anand Rajaraman, Jeffrey David Ullman.
Summary
The popularity of the Web and Internet commerce provides many extremely large datasets from which infomration can be gleaned by data mining. This book focuses on practical algorithms that have been used to slve key problems in data mining and can be used on even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The tricks of locality-sensitive hashing are explained. This body of knowledge, which deserves to be more widel known, is essential when seeking similar objects in a very large collection without having to compare each pair of objects. Stream processing algorithms for mining data that arrives too fast or for exhaustive processing are also explained. The PageRank idea and related tricks for organizing the Web are covered next. Other chapters cover the problems of finding frequent itemsets and clustering, each from the point of view that the data is too large to fit in main memory. The final chapters cover two applications: recommendation systems and Web advertising, each vital in e-commerce. Written by two authorities in database and web technologies, this book will be essential for students and practitioners alike. --
Read More ↓Edition Statements
- Publication Date:©2012
- Edition:First edition
Publication Statements
- Publisher(s):Cambridge University Press
- Place of Publication:New York, NY
- Language:eng
- Publication Date:©2012
The Physical Object
- Description:x, 315 pages : : illustrations ; : 26 cm
Edition Identifiers
- ISBN:9781107015357, 1107015359
- oclc:ocn769542667
- LC Classification:QA76.9.D343
- Dewey Decimal:006.312
Table of Contents
Data mining -- Large-scale file systems and map-reduce -- Finding similar items -- Mining data streams -- Link analysis -- Frequent itemsets -- Clustering -- Advertising on the web -- Recommendation systems.
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