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. --
Description
Mining of Massive Datasets: 9781107077232: Computer Science Books @ Amazon.com
%0 Book
%1 leskovec2014mining
%A Leskovec, Jure
%A Rajaraman, Anand
%A Ullman, Jeffrey D.
%D 2014
%I Cambridge University Press
%K approximation bigdata bigdataseminar
%T Mining of Massive Datasets
%U http://mmds.org
%X 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. --
%7 Second
%@ 9781316147313 1316147312 9781139924801 113992480X 9781316147047 1316147045 1107077230 9781107077232
@book{leskovec2014mining,
abstract = {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. --},
added-at = {2016-10-11T19:05:15.000+0200},
author = {Leskovec, Jure and Rajaraman, Anand and Ullman, Jeffrey D.},
biburl = {https://www.bibsonomy.org/bibtex/2cbefb4251478e2895c8abf63809f9108/schmitz},
description = {Mining of Massive Datasets: 9781107077232: Computer Science Books @ Amazon.com},
edition = {Second},
interhash = {2c22575d85b455103c98079f42558947},
intrahash = {cbefb4251478e2895c8abf63809f9108},
isbn = {9781316147313 1316147312 9781139924801 113992480X 9781316147047 1316147045 1107077230 9781107077232},
keywords = {approximation bigdata bigdataseminar},
publisher = {Cambridge University Press},
refid = {888463433},
timestamp = {2016-12-19T20:44:06.000+0100},
title = {Mining of Massive Datasets},
url = {http://mmds.org},
year = 2014
}