Mining of massive datasets

By:

Leskovec, Jure

Contributor(s):

Publication details: Cambridge University Press, 2020. Cambridge:Edition: 3rd edDescription: xi, 553p.: ill.; hbk; 25cmISBN:

9781108476348

Subject(s):

DDC classification:

006.312 LES

Summary: 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. https://www.cambridge.org/core/books/mining-of-massive-datasets/C1B37BA2CBB8361B94FDD1C6F4E47922#fndtn-information

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Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Books	IIT Gandhinagar	General	006.312 LES (Browse shelf(Opens below))	1	Available		032879

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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.

https://www.cambridge.org/core/books/mining-of-massive-datasets/C1B37BA2CBB8361B94FDD1C6F4E47922#fndtn-information

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