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Mining of massive datasets
~
Leskovec, Jurij.
Mining of massive datasets
紀錄類型:
書目-語言資料,印刷品 : 單行本
作者:
LeskovecJurij.,
其他作者:
RajaramanAnand.,
其他作者:
UllmanJeffrey D., 1942-
出版地:
Cambridge, United Kingdom
出版者:
Cambridge University Press;
出版年:
c2020.
版本:
3rd ed.
面頁冊數:
xi, 553 p.ill. : 26 cm.;
標題:
Data mining. -
附註:
Previous ed. in 2014.
摘要註:
"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.
ISBN:
9781108476348
內容註:
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, Jurij.
Mining of massive datasets
/ Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman. - 3rd ed.. - Cambridge, United Kingdom : Cambridge University Press, c2020.. - xi, 553 p. ; ill. ; 26 cm..
Data mining.
Previous ed. in 2014..
Includes bibliographical references and index..
ISBN 9781108476348ISBN 1108476341
Data mining.
Rajaraman, Anand.
Mining of massive datasets
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Data mining
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Mining data streams
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Frequent itemsets
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Clustering
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Advertising on the Web
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Recommendation systems
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Mining social-network graphs
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Dimensionality reduction
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Large-scale machine learning
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Neural nets and deep learning.
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"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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