MARC details
| 000 -LEADER |
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| fixed length control field |
02081nam a22004097a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
CUTN |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20210628110814.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
210628b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781316638491 |
| 041 ## - LANGUAGE CODE |
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| Language |
English |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.312 |
| Item number |
LES |
| Edition number |
23 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Leskovec, Jure. |
| 245 ## - TITLE STATEMENT |
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| Title |
Mining of Massive Datasets / |
| Statement of responsibility, etc |
Jure Leskovec ; Ananand Rajaraman ; Jeffrey David Ullman. |
| 250 ## - EDITION STATEMENT |
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| Edition statement |
2nd ed. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc |
New Delhi : |
| Name of publisher, distributor, etc |
Cambridge University Press, |
| Date of publication, distribution, etc |
2014. |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xii, 467 p. : |
| Other physical details |
ill. ; |
| Dimensions |
24 cm. |
| 500 ## - GENERAL NOTE |
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| General note |
This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to 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. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets and clustering. This second edition includes new and extended coverage on social networks, machine learning and dimensionality reduction. It includes a range of over 150 challenging exercises |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Title |
Data mining --<br/> |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Title |
MapReduce and the new software stack --<br/> |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Title |
Finding similar items --<br/> |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Title |
Mining data streams --<br/><br/> |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Title |
Link analysis --<br/> |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Title |
Frequent itemsets --<br/><br/><br/> |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Title |
Clustering --<br/><br/><br/><br/><br/> |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Title |
Advertising on the Web --<br/><br/><br/><br/><br/> |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Title |
Recommendation systems --<br/><br/><br/><br/><br/><br/> |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Title |
Mining social-network graphs --<br/><br/><br/><br/><br/><br/> |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Title |
Dimensionality reduction --<br/><br/><br/><br/><br/><br/><br/><br/><br/> |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Title |
Large-scale machine learning.<br/><br/><br/><br/><br/><br/><br/><br/> |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Data mining. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
R.C.C Books. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Big data. |
| 690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN) |
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| Department Name |
Statistics. |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Rajaraman, Anand. |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Ullman, Jeffrey David. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
General Books |
