Description: Mining of Massive Datasets, Hardcover by Leskovec, Jure; Rajaraman, Anand; Ullman, Jeffrey David, ISBN 1108476341, ISBN-13 9781108476348, Brand New, Free shipping in the US "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 tofit 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"--
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Book Title: Mining of Massive Datasets
Number of Pages: 565 Pages
Language: English
Publication Name: Mining of Massive Data Sets
Publisher: Cambridge University Press
Item Height: 1.1 in
Publication Year: 2020
Subject: Databases / Data Mining, Computer Vision & Pattern Recognition
Features: Revised
Type: Textbook
Item Weight: 43.7 Oz
Item Length: 10 in
Author: Anand Rajaraman, Jure Leskovec, Jeffrey David Ullman
Subject Area: Computers
Item Width: 7 in
Format: Hardcover
