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Mining of Massive Datasets (Anglais) Relié – 27 octobre 2011

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Présentation de l'éditeur

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 which can be used on 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. The PageRank idea and related tricks for organizing the Web are covered next. Other chapters cover the problems of finding frequent itemsets and clustering. The final chapters cover two applications: recommendation systems and Web advertising, each vital in e-commerce. Written by two authorities in database and Web technologies, this book is essential reading for students and practitioners alike.

Biographie de l'auteur

Anand Rajaraman is CEO of Kosmix Inc., a website which organizes the Internet by topic. He is also a consulting assistant professor in the Computer Science Department at Stanford University. In 1996, together with four other engineers, Rajaraman founded Junglee Corp., which pioneered Internet comparison shopping. It was acquired by Amazon.com Inc. in August 1998 for 1.6 million shares of stock valued at $250 million. Rajaraman went on to become Director of Technology at Amazon.com, where he was responsible for technology strategy. He 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. Third-party transactions now account for almost 25% of all US transactions, and represent Amazon's fastest-growing and most profitable business segment. Rajaraman was also an inventor of the concept underlying Amazon.com's Mechanical Turk. Rajaraman and his business partner, Venky Harinarayan, co-founded Cambrian Ventures, an early stage VC fund, in 2000. Cambrian went on to back several companies later acquired by Google and has funded companies like Mobissimo, Aster Data Systems and TheFind.com.

Jeffrey David Ullman is the Stanford W. Ascherman Professor of Computer Science (Emeritus) at Stanford University. He is also the CEO of Gradiance. Ullman's research interests include database theory, data integration, 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 also the Ph.D. advisor of Sergey Brin, one of the co-founders of Google, and served on Google's technical advisory board. In 1995 he was inducted as a Fellow of the Association for Computing Machinery and in 2000 he was awarded the Knuth Prize. 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'.

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30 internautes sur 34 ont trouvé ce commentaire utile 
Good choice of topics, poorly written 27 août 2012
Par AnMLS - Publié sur Amazon.com
Format: Relié
The authors of this book are authorities in their respective fields. I bought this book for specifically the more "modern" machine learning topics like hashing techniques, stream mining and for software platforms that are becoming widely accepted like hadoop. Unfortunately, these techniques were only superficially covered and do not provide a comprehensive overview of the state-of-art.

It was also disappointing to see that some of the chapters were not comprehensive (e.g., the chapter on "advertising on the web" only contains some search advertising while completely ignoring models for display advertisement) and some of the more modern developments in recommendation techniques are left out from the Recommendation Systems chapter. For Recommender systems, I was hoping for coverage of scalable ML techniques tailored to high volume, low latency, and online learning requirements. The Clustering chapter has not enough depth as far as scaling up to massive datasets is concerned.

There are also some typos and printing errors in the printed hardbound version that seem to have been updated in the free online version of the book.
3 internautes sur 3 ont trouvé ce commentaire utile 
Great Middle-Ground Text 25 août 2014
Par TxF - Publié sur Amazon.com
Format: Relié
This is a good middle-ground book: it connects the realm of rigorous and mathematically correct research papers with a non-technical (a.k.a. pointy-hair friendly) explanation of the concepts.

I have graduate degree in data mining and information retrieval; my primary use for this book has been to help explain the "why" and "how" behind some of our implementation choices (with related expenses) to management. The authors do a good job of not only presented technically accurate material, but sufficiently motivating the concepts for the arithmetically challenged among us. The text flows well and is easy to digest, the authors didn't just regurgitate gobs of indecipherable summations and product formulas.

While I can sympathize with some of the reviews wanting a book to connect the theoretical with the practical, this isn't it-- and I can't help but to believe that wasn't its intention. If you want a book to teach you how to do your job or implement a concept in code, buy a technology-specific book. If you want to understand the basic math and motivation for choosing one approach over another, this book provides a sound foundation.

One thing I would have liked to have seen more of, perhaps in the appendix materials, are some non-trivial step-by-step algorithm explanations. Bound by size limitations (which I totally appreciate not having an 800p book) some of the examples are too simple to illustrate a clear step-by-step execution of some of the more complex examples where edge cases may get lost (e.g., Locality Sensitive Functions, Clustering, etc.).
0 internautes sur 1 ont trouvé ce commentaire utile 
The authors of this book also host a free online ... 15 février 2015
Par Liaosa - Publié sur Amazon.com
Format: Relié Achat vérifié
The authors of this book also host a free online course on Coursera.org. https://www.coursera.org/course/mmds I purchased this text as a companion of this course. It is quite help understand the materials covered in this online course and not difficult to read. I hope in the future, the authors can add some practical implementation of the algorithm discussed in the text.
2 internautes sur 7 ont trouvé ce commentaire utile 
Lots of information not much practical application 31 août 2013
Par Steve Yurkiewicz - Publié sur Amazon.com
Format: Format Kindle Achat vérifié
Enjoyed the book but I didn't think there was enough on practical application of the topics. Very text book like.
1 internautes sur 7 ont trouvé ce commentaire utile 
It's good 4 juin 2013
Par Eugene Morozov - Publié sur Amazon.com
Format: Relié Achat vérifié
Especially I like chapter 2 with complete description of Pig-Latin and Hadoop. Really cool stuff, helped me to understand how to program Pig-Latin.
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