Mining of massive datasets.
Jurij Leskovec ; Anand Rajaraman ; Jeffrey D Ullman
Mining of massive datasets. /Jurij Leskovec - 2nd edition - Cambridge : Cambridge University Press , 2014. - xii, 467 pages ;24 cm.
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.
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. It includes a range of over 150 challenging exercises.
9781316638491
Atlantic Publishers & Distributors (P)LTD. ;7/22,Anasri Road, Daryaganj,New Delhi-110002
Computer Science
Data mining--Big data
006.312 LES
Mining of massive datasets. /Jurij Leskovec - 2nd edition - Cambridge : Cambridge University Press , 2014. - xii, 467 pages ;24 cm.
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.
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. It includes a range of over 150 challenging exercises.
9781316638491
Atlantic Publishers & Distributors (P)LTD. ;7/22,Anasri Road, Daryaganj,New Delhi-110002
Computer Science
Data mining--Big data
006.312 LES
