02274nam a22002537a 4500 20260915145557.0 260915m20202012|||||||| |||| 00| 0 eng d 9781108476348 PK-LaUMT 006.312 LES-M Leskovec, Jure 15525 Mining of massive datasets / Jure Leskovec, Anand Rajaraman and Jeffrey D. Ullman 3rd ed. Cambridge : Cambridge University Press, 2020 xi, 553 p. Includes bibliographical references and index. "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"-- Provided by publisher Eng Big Data Data Mining Rajaraman, Anand 15526 Ullman, Jeffrey D. 15527 BK 142527 142527 0 0 0 0 01 01 2026-09-15 0 T 006.312 LES-M 154399 2026-09-15 14:47:09 2026-09-15 BK 0 0 0 0 01 01 2026-09-15 0 T 006.312 LES-M 154400 2026-09-15 14:47:16 C.2 2026-09-15 BK 0 0 0 0 01 01 2026-09-15 0 T 006.312 LES-M 154401 2026-09-15 14:47:23 C.3 2026-09-15 BK 0 0 0 0 01 01 2026-09-15 0 T 006.312 LES-M 154402 2026-09-15 14:47:31 C.4 2026-09-15 BK 0 0 0 0 01 01 2026-09-15 0 T 006.312 LES-M 154403 2026-09-15 14:47:45 C.5 2026-09-15 BK