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Mining of massive datasets

[NT 42944] Record Type:	[NT 1579] Language materials, printed : [NT 40817] monographic
[NT 47261] Author:	LeskovecJure,
[NT 47353] Alternative Intellectual Responsibility:	RajaramanAnand,
[NT 47353] Alternative Intellectual Responsibility:	UllmanJeffrey David,
[NT 47351] Place of Publication:	Cambridge, UK
[NT 47263] Published:	Cambridge University Press;
[NT 47352] Year of Publication:	2020
[NT 50960] Edition:	Third edition
[NT 47264] Description:	xi, 553 p.ill. : 26 cm.;
[NT 47266] Subject:	Data mining -
[NT 51398] Summary:	"The Web, social media, mobile activity, sensors, Internet commerce, and many other modern applications provide 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 used on even the largest datasets. It begins with a discussion of the MapReduce framework and related techniques for efficient parallel programming. The tricks of locality-sensitive hashing are explained. This body of knowledge, which deserves to be more widely 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 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. Two applications: recommendation systems and Web advertising, each vital in e-commerce, are treated in detail. Later chapters cover algorithms for analyzing social-network graphs, compressing large-scale data, and machine learning. This third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs. Written by leading authorities in database and Web technologies, it is essential reading for students and practitioners alike"--Provided by publisher
[NT 50961] ISBN:	978-1-108-47634-8bound
[NT 60779] Content Note:	Data mining MapReduce and the new software stack Finding similar items Mining data streams Link analysis Frequent itemsets Clustering Advertising on the Web Recommendation systems Mining social-network graphs Dimensionality reduction Large-scale machine learning Neural nets and deep learning

Mining of massive datasets
Leskovec, Jure

Mining of massive datasets / Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman - Third edition. - Cambridge, UK : Cambridge University Press, 2020. - xi, 553 p. ; ill. ; 26 cm..
Data mining.
Includes bibliographical references and index..
ISBN 978-1-108-47634-8
Data mining

Rajaraman, Anand

Mining of massive datasets
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320 $a Includes bibliographical references and index.
327 1 $a Data mining $a MapReduce and the new software stack $a Finding similar items $a Mining data streams $a Link analysis $a Frequent itemsets $a Clustering $a Advertising on the Web $a Recommendation systems $a Mining social-network graphs $a Dimensionality reduction $a Large-scale machine learning $a Neural nets and deep learning
330 $a "The Web, social media, mobile activity, sensors, Internet commerce, and many other modern applications provide 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 used on even the largest datasets. It begins with a discussion of the MapReduce framework and related techniques for efficient parallel programming. The tricks of locality-sensitive hashing are explained. This body of knowledge, which deserves to be more widely 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 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. Two applications: recommendation systems and Web advertising, each vital in e-commerce, are treated in detail. Later chapters cover algorithms for analyzing social-network graphs, compressing large-scale data, and machine learning. This third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs. Written by leading authorities in database and Web technologies, it is essential reading for students and practitioners alike"--Provided by publisher
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