000 nam a22 4500
999 _c18446
_d18446
005 20221220111850.0
008 221220b xxu||||| |||| 00| 0 eng d
020 _a9781108476348
040 _cAIKTC-KRRC
041 _aENG
082 _2DDC23
_a006.312
_bLES
100 _919370
_aLeskovec, Jure
245 _aMining of massive datasets
250 _a3rd
260 _aCambridge
_bCambridge University Press
_c2020
300 _axi, 553p.
_bHard Bound
_c25*18 cm
520 _a"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"--
650 0 _94622
_aComputer Engineering
700 _919372
_aRajaraman, Anand
700 _94675
_aUllman, Jeffrey D.
856 _uhttp://www.mmds.org/
_zeBook by Publisher
942 _2ddc
_cBK