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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.
- Sales Rank: #75974 in Books
- Published on: 2014-12-29
- Original language: English
- Number of items: 1
- Dimensions: 9.72" h x 1.18" w x 6.85" l, 2.18 pounds
- Binding: Hardcover
- 476 pages
About the Author
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.
Anand Rajaraman is a serial entrepreneur, venture capitalist, and academic based in Silicon Valley. He is a Founding Partner of two early-stage venture capital firms, Milliways Labs and Cambrian Ventures. His investments include Facebook (one of the earliest angel investors in 2005), Aster Data Systems (acquired by Teradata), Efficient Frontier (acquired by Adobe), Neoteris (acquired by Juniper), Transformic (acquired by Google), and several others. Anand was, until recently, Senior Vice President at Walmart Global eCommerce and co-head of @WalmartLabs, where he worked at the intersection of social, mobile, and commerce. He came to Walmart when Walmart acquired Kosmix, the startup he co-founded, in 2011. Kosmix pioneered semantic search technology and semantic analysis of social media. In 1996, Anand co-founded Junglee, an e-commerce pioneer. As Chief Technology Officer, he played a key role in developing Junglee's award-winning Virtual Database technology. In 1998, Amazon.com acquired Junglee, and Anand helped launch the transformation of Amazon.com from a retailer into a retail platform, enabling third-party retailers to sell on Amazon.com's website. Anand is also a co-inventor of Amazon Mechanical Turk, which pioneered the concepts of crowdsourcing and hybrid Human-Machine computation. As an academic, Anand's research has focused at the intersection of database systems, the World-Wide Web, and social media. His research publications have won several awards at prestigious academic conferences, including two retrospective 10-year Best Paper awards at ACM SIGMOD and VLDB. In 2012, Fast Company magazine named Anand to its list of '100 Most Creative People in Business'. In 2013, he was named a Distinguished Alumnus by his alma mater, IIT Madras. You can follow Anand on Twitter at @anand_raj.
Jeffrey David Ullman is the Stanford W. Ascherman Professor of Computer Science (Emeritus) and he is currently the CEO of Gradiance. His research interests include database theory, data mining, and education using the information infrastructure. He is one of the founders of the field of database theory, and was the doctoral advisor of an entire generation of students who later became leading database theorists in their own right. He was the Ph.D. advisor of Sergey Brin, one of the co-founders of Google, and served on Google's technical advisory board. Ullman was elected to the National Academy of Engineering in 1989, the American Academy of Arts and Sciences in 2012, and he has held Guggenheim and Einstein Fellowships. Recent awards include the Knuth Prize (2000), and the Sigmod E. F. Codd Innovations award (2006). Ullman is also the co-recipient (with John Hopcroft) of the 2010 IEEE John von Neumann Medal, for 'laying the foundations for the fields of automata and language theory and many seminal contributions to theoretical computer science'.
Most helpful customer reviews
6 of 7 people found the following review helpful.
An assortment of heuristics and algorithms
By Yow-Bang (Darren) Wang
As the textbook of the Stanford online course of same title, this books is an assortment of heuristics and algorithms from data mining to some big data applications nowadays. I think this book can be especially suitable for those who:
1. Have some machine learning background and want to have a quick glance over every popular data mining techniques;
2. Have learned data mining and need to quickly look up some phrases along with compact explanations.
In other word, I don't think this book is for those who wish to see rigorous mathematical elements because frankly the content far from that; also, if you're totally new to machine learning or data mining, you can take your first step from here, but it'll be a struggled step I would guess. However, if you're buying this book to go with the online course, then this is a great complement.
4 of 4 people found the following review helpful.
A wonderful reference book
By Sneha
This book is a delight for anyone who deals with practical Data Mining applications. Over the past few years, I have gathered bits and pieces of knowledge from various sources about machine learning, Map Reduce programming paradigm, design and analysis of algorithms, information retrieval, etc. But this book serves to tie it all together beautifully. If you have delved in the above topics and are looking for a reference book that strikes a balance between rigor and practicality, this book will serve you right. On the other hand, if you are just starting out in the field of Data Mining/Machine Learning then you may do well by starting out with more detailed material.
The book has a nice compilation of many "greatest hits" algorithms, especially those related to mining graph data. The book treats the theory and the implementation aspects of algorithms with equal importance with ample consideration for scaling.The examples in the book are very intuitive and the book follows an easy to understand train of thought. The chapter summaries are a pleasant surprise. They are a great resource to help you distill and digest the key points from each chapter. The summaries are succinct enough to be un-intimidating and are descriptive enough to be useful.
The book does keep referring back and forth between chapters but that is only because much of the material is actually interlinked and treating the topics in isolation would miss the point.
All in all a great purchase for a lifetime!
1 of 1 people found the following review helpful.
Good Textbook at a Reasonable Price
By Dwayne Phillips
First, the book is affordable at under $70. That is a big deal. You can download a PDF for free at several sites, but printing it would cost you $70 and the physical package would not be nearly as good. This is a significant physical hardback book.
Content, they cover a lot of topics.
I like the way the chapters are arranged. There are summaries at the end of every chapter. I found myself reading the summaries of topics before reading the pertinent sections and then reading the summaries again section by section. I learned much more using that practice instead of simply reading cover to cover in order.
This is a good book. It is a good substitute for any number of online learning programs in data science.
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