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Mining of Massive Datasets

Autor Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman
en Limba Engleză Hardback – 9 ian 2020
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 MapReduce 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 third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs.
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Specificații

ISBN-13: 9781108476348
ISBN-10: 1108476341
Pagini: 565
Ilustrații: 76 b/w illus. 250 exercises
Dimensiuni: 178 x 253 x 28 mm
Greutate: 1.23 kg
Ediția:3Revizuită
Editura: Cambridge University Press
Colecția Cambridge University Press
Locul publicării:Cambridge, United Kingdom

Cuprins

1. Data mining; 2. MapReduce and the new software stack; 3. Finding similar items; 4. Mining data streams; 5. Link analysis; 6. Frequent itemsets; 7. Clustering; 8. Advertising on the web; 9. Recommendation systems; 10. Mining social-network graphs; 11. Dimensionality reduction; 12. Large-scale machine learning; 13. Neural nets and deep learning; Index.

Notă biografică


Descriere

Now in its third edition, this book focuses on practical algorithms for mining data from even the largest datasets.