Mining of Massive Datasets pdf epub mobi txt 电子书下载 2025

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Jure Leskovec is Assistant Professor of Computer Science at Stanford University. His research focuses on mining large social and information networks. Problems he investigates are motivated by large scale data, the Web and on-line media. This research has won several awards including a Microsoft Research Faculty Fellowship, the Alfred P. Sloan Fellowship, Okawa Foundation Fellowship, and numerous best paper awards. His research has also been featured in popular press outlets such as the New York Times, the Wall Street Journal, the Washington Post, MIT Technology Review, NBC, BBC, CBC and Wired. Leskovec has also authored the Stanford Network Analysis Platform (SNAP, http://snap.stanford.edu), a general purpose network analysis and graph mining library that easily scales to massive networks with hundreds of millions of nodes and billions of edges. You can follow him on Twitter at @jure.

出版者:Cambridge University Press

作者:Jure Leskovec

出品人:

页数:476

译者:

出版时间:2014-12-29

价格:USD 75.99

装帧:Hardcover

isbn号码:9781107077232

丛书系列:

图书标签:

数据挖掘
计算机
机器学习
Data
Coursera
CS
数据分析
软件工程

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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 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.

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当今时代大规模数据爆炸的速度是惊人的，当然，其应用也是越来越广泛的，从传统的零售业到复杂的商业世界，到处都能见到它的身影。那么大数据有什么典型特征呢？即数据类型繁多、数据体量巨大、价值密度低即处理速度快。本书也正是将注意力集中在了极大规模数据上的挖掘，而且...

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从总体安排来看，书的结构还是不错的。没看过英文的，但是中文版的行文真的不好，磕磕绊绊看了一半以后实在是没有兴趣看后面的了。之前了解的pagerank看了以后了解了，之前不了解的adwords还是不了解，

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内容是算法分析应该有的套路, 对于Correctness, Running Time, Storage的证明; 讲得很细, 一个星期要讲3个算法, 看懂以后全部忘光大概率要发生. 要是能多给些直觉解释就好了. Ullman的表达绝对是有问题的, 谁不承认谁就是不客观, 常常一句话我要琢磨2个小时, 比如DGIM算法有一...

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