Mining of massive datasets (Record no. 18446)

MARC details
000 -LEADER
fixed length control field nam a22 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20221220111850.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 221220b xxu||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781108476348
040 ## - CATALOGING SOURCE
Transcribing agency AIKTC-KRRC
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title ENG
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Edition number DDC23
Classification number 006.312
Item number LES
100 ## - MAIN ENTRY--PERSONAL NAME
9 (RLIN) 19370
Personal name Leskovec, Jure
245 ## - TITLE STATEMENT
Title Mining of massive datasets
250 ## - EDITION STATEMENT
Edition statement 3rd
260 ## - PUBLICATION, DISTRIBUTION, ETC.
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, 553p.
Other physical details | Binding- Hard Bound |
Dimensions 25*18 cm
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"--
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
9 (RLIN) 4622
Topical term or geographic name entry element Computer Engineering
700 ## - ADDED ENTRY--PERSONAL NAME
9 (RLIN) 19372
Personal name Rajaraman, Anand
700 ## - ADDED ENTRY--PERSONAL NAME
9 (RLIN) 4675
Personal name Ullman, Jeffrey D.
856 ## - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="http://www.mmds.org/">http://www.mmds.org/</a>
Public note eBook by Publisher
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Books
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Total Checkouts Full call number Barcode Date last seen Cost, replacement price Price effective from Koha item type
    Dewey Decimal Classification     Reference School of Engineering & Technology School of Engineering & Technology Reference Section 20/12/2022 22 4651.25   006.312 LES E15781 25/06/2025 6201.67 20/12/2022 Books
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