000			01851nam a22002417a 4500
005			20180917152913.0
008			180917b xxu||||| |||| 00| 0 eng d
022			_a9781316638491
028			_aAtlantic Publishers & Distributors (P)LTD. _b;7/22,Anasri Road, Daryaganj,New Delhi-110002 _c;1082225 _d;05/09/2018
082			_a006.312 LES
100			_aJurij Leskovec _a; Anand Rajaraman _a; Jeffrey D Ullman
245			_aMining of massive datasets. _c/Jurij Leskovec
250			_a2nd edition
260			_aCambridge _b: Cambridge University Press _c, 2014.
300			_axii, 467 pages _c;24 cm.
365			_b649
505			_a 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.
520			_a 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.
650			_aComputer Science
655			_aData mining _bBig data
700			_aAnand Rajaraman; Jeffrey D Ullman
942			_cBOOK
999			_c50166 _d50166