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_d73578
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008 210823b ||||| |||| 00| 0 eng d
020 _a9781139058452
041 _aeng.
082 _a006.312
100 1 _aRajaraman, Anand.
_eauthor
245 _aMining of massive datasets /
_cAnand Rajaraman, Jeffrey David Ullman.
264 1 _aCambridge:
_bCambridge University Press,
_cc2012.
300 _a1 online resource (327 pages) :
336 _2rdacontent
_atext
_btext
337 _2rdamedia
_acomputer
_bc
338 _2rdacarrier
_aonline resource
_bcr
504 _aIncludes bibliographical references and index.
505 _aData mining -- Large-scale file systems and map-reduce -- Finding similar items -- Mining data streams -- Link analysis -- Frequent itemsets -- Clustering -- Advertising on the web -- Recommendation systems.
520 _aThe popularity of the Web and Internet commerce provides 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 which can be used on 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. The PageRank idea and related tricks for organizing the Web are covered next. Other chapters cover the problems of finding frequent itemsets and clustering. The final chapters cover two applications: recommendation systems and Web advertising, each vital in e-commerce. Written by two authorities in database and Web technologies, this book is essential reading for students and practitioners alike.
650 0 _aData mining.
650 0 _aComputer algorithms.
655 0 _aElectronic books.
700 1 _aUllman, Jeffrey D.
_d1942-
_eauthor
856 _yFull text available at Cambridge Online Library Click here to view
_uhttps://www.cambridge.org/core/books/mining-of-massive-datasets/A06D57FC616AE3FD10007D89E73F8B92
942 _2ddc
_cER