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Mining of Massive Datasets by Jure Leskovec (English) Hardcover Book

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Last updated on 26 Jun, 2025 06:20:22 BSTView all revisionsView all revisions

Item specifics

Condition
New: A new, unread, unused book in perfect condition with no missing or damaged pages. See the ...
ISBN-13
9781108476348
Book Title
Mining of Massive Datasets
ISBN
9781108476348

About this product

Product Identifiers

Publisher
Cambridge University Press
ISBN-10
1108476341
ISBN-13
9781108476348
eBay Product ID (ePID)
12038707628

Product Key Features

Number of Pages
565 Pages
Language
English
Publication Name
Mining of Massive Data Sets
Publication Year
2020
Subject
Databases / Data Mining, Computer Vision & Pattern Recognition
Features
Revised
Type
Textbook
Author
Anand Rajaraman, Jure Leskovec, Jeffrey David Ullman
Subject Area
Computers
Format
Hardcover

Dimensions

Item Height
1.1 in
Item Weight
43.7 Oz
Item Length
10 in
Item Width
7 in

Additional Product Features

Edition Number
3
Intended Audience
College Audience
LCCN
2020-012035
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
006.312
Edition Description
Revised edition
Table Of Content
1. Data mining; 2. MapReduce and the new software stack; 3. Finding similar items; 4. Mining data streams; 5. Link analysis; 6. Frequent itemsets; 7. Clustering; 8. Advertising on the web; 9. Recommendation systems; 10. Mining social-network graphs; 11. Dimensionality reduction; 12. Large-scale machine learning; 13. Neural nets and deep learning; Index.
Synopsis
Essential reading for students and practitioners, this book focuses on practical algorithms used to solve key problems in data mining, with exercises suitable for students from the advanced undergraduate level and beyond. 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, this book is essential reading for students and practitioners alike. The popularity of the Web and Internet commerce provides 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 applied successfully to even the largest datasets. It begins with a discussion of the MapReduce 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 third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs.
LC Classification Number
HF5415.125.L46 2020

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