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Mining of Massive Datasets (Relié)
2nd edition
Edition en anglais
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Résumé
The Web, social media, mobile activity, sensors, Internet commerce and so on all 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, an important tool for parallelizing algorithms automatically.
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, and two applications : recommendation systems and Web advertising, each vital in e-commerce. This second edition includes new and extended coverage on social networks, machine learning and dimensionality reduction. Written by leading authorities in database and web technologies, it is essential reading for students and practitioners alike.
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, and two applications : recommendation systems and Web advertising, each vital in e-commerce. This second edition includes new and extended coverage on social networks, machine learning and dimensionality reduction. Written by leading authorities in database and web technologies, it is essential reading for students and practitioners alike.
Fiche technique
- Date de parution : 01/11/2014
- Editeur : Cambridge University Press
- ISBN : 978-1-107-07723-2
- EAN : 9781107077232
- Format : Grand Format
- Présentation : Relié
- Nb. de pages : 467 pages
- Poids : 1.125 Kg
- Dimensions : 18,0 cm × 25,3 cm × 2,7 cm
À propos des auteurs
Jure Leskovec is Assistant Professor of Computer Science at Stanford University. His research focuses on mining large social and information networks. Problems he investigates are motivated by large scale data, the Web and on-line media. This research has won several awards including a Microsoft Research Faculty Fellowship, the Alfred P. Sloan Fellowship, Okawa Foundation Fellowship, and numerous best paper awards.
Leskovec has also authored the Stanford Network Analysis Platform (SNAP, http :// snap.stanford.edu), a general purpose network analysis and graph mining library that easily scales to massive networks with hundreds of millions of nodes and billions of edges. You can follow him on Twitter at @jure. Anand Rajaraman is a serial entrepreneur, venture capitalist, and academic based in Silicon Valley. He has founded two successful startups : Junglee, acquired by Amazon.com and Kosmix, acquired by Walmart.
As a Founding Partner of two early-stage venture capital firms, Milliways Labs and Cambrian Ventures, he has been the earliest investor in many successful companies. Anand was, until recently, Senior Vice President at Walmart Global eCommerce and co-head of ©WalmartLabs, where he worked at the intersection of social, mobile, and commerce. As an academic, Anand's research has focused at the intersection of database systems, the World Wide Web, and social media.
His research publications have won several awards at prestigious academic conferences, including two retrospective 10-year Best Paper awards at ACM SIGMOD and VLDB. He is also a co-inventor of Amazon Mechanical Turk, which pioneered the concept of crowdsourcing. You can follow Anand on Twitter at ©anand_raj. Jeffrey David Ullman is the Stanford W. Ascherman Professor of Computer Science (Emeritus) and he is currently the CEO of Gradiance.
His research interests include database theory, data mining, and education using the information infrastructure. He is one of the founders of the field of database theory, and was the doctoral advisor of an entire generation of students who later became leading database theorists in their own right. Recent awards include the Knuth Prize (2000), and the Sigmod E. F. Codd Innovations award (2006). Ullman is also the co-recipient (with John Hoperoft) of the 2010 IEEE John von Neumann Medal, for "laying the foundations for the fields of automata and language theory and many seminal contributions to theoretical computer science."
Leskovec has also authored the Stanford Network Analysis Platform (SNAP, http :// snap.stanford.edu), a general purpose network analysis and graph mining library that easily scales to massive networks with hundreds of millions of nodes and billions of edges. You can follow him on Twitter at @jure. Anand Rajaraman is a serial entrepreneur, venture capitalist, and academic based in Silicon Valley. He has founded two successful startups : Junglee, acquired by Amazon.com and Kosmix, acquired by Walmart.
As a Founding Partner of two early-stage venture capital firms, Milliways Labs and Cambrian Ventures, he has been the earliest investor in many successful companies. Anand was, until recently, Senior Vice President at Walmart Global eCommerce and co-head of ©WalmartLabs, where he worked at the intersection of social, mobile, and commerce. As an academic, Anand's research has focused at the intersection of database systems, the World Wide Web, and social media.
His research publications have won several awards at prestigious academic conferences, including two retrospective 10-year Best Paper awards at ACM SIGMOD and VLDB. He is also a co-inventor of Amazon Mechanical Turk, which pioneered the concept of crowdsourcing. You can follow Anand on Twitter at ©anand_raj. Jeffrey David Ullman is the Stanford W. Ascherman Professor of Computer Science (Emeritus) and he is currently the CEO of Gradiance.
His research interests include database theory, data mining, and education using the information infrastructure. He is one of the founders of the field of database theory, and was the doctoral advisor of an entire generation of students who later became leading database theorists in their own right. Recent awards include the Knuth Prize (2000), and the Sigmod E. F. Codd Innovations award (2006). Ullman is also the co-recipient (with John Hoperoft) of the 2010 IEEE John von Neumann Medal, for "laying the foundations for the fields of automata and language theory and many seminal contributions to theoretical computer science."
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Mining of Massive Datasets 2nd edition
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