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Mining of massive datasets / (Record no. 142527)

MARC details
000 -LEADER
fixed length control field 02274nam a22002537a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20260915145557.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 260915m20202012|||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781108476348
040 ## - CATALOGING SOURCE
Transcribing agency PK-LaUMT
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.312
Item number LES-M
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Leskovec, Jure
245 10 - TITLE STATEMENT
Title Mining of massive datasets /
Statement of responsibility, etc Jure Leskovec, Anand Rajaraman and Jeffrey D. Ullman
250 ## - EDITION STATEMENT
Edition statement 3rd ed.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc Cambridge :
Name of publisher, distributor, etc Cambridge University Press,
Date of publication, distribution, etc 2020
300 ## - PHYSICAL DESCRIPTION
Extent xi, 553 p.
500 ## - GENERAL NOTE
General note Includes bibliographical references and index.
520 ## - SUMMARY, ETC.
Summary, etc "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
546 ## - LANGUAGE NOTE
Language note Eng
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Big Data
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data Mining
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Rajaraman, Anand
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Ullman, Jeffrey D.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Books
Holdings
Withdrawn status Lost status Damaged status Home library Current library Date acquired Full call number Barcode Date last seen Price effective from Koha item type Copy number
      UMT Main Campus UMT Main Campus 2026-09-15 T 006.312 LES-M 154399 2026-09-15 2026-09-15 Books  
      UMT Main Campus UMT Main Campus 2026-09-15 T 006.312 LES-M 154400 2026-09-15 2026-09-15 Books C.2
      UMT Main Campus UMT Main Campus 2026-09-15 T 006.312 LES-M 154401 2026-09-15 2026-09-15 Books C.3
      UMT Main Campus UMT Main Campus 2026-09-15 T 006.312 LES-M 154402 2026-09-15 2026-09-15 Books C.4
      UMT Main Campus UMT Main Campus 2026-09-15 T 006.312 LES-M 154403 2026-09-15 2026-09-15 Books C.5