Mining of Massive Datasets
Mining of Massive Datasets
by Anand Rajaraman, Jeffrey D. Ullman
eBook Details:
Publisher: Stanford University 2010
Number of pages: 340
License(s) : Pending review
eBook Description:
At the highest level of description, this book is about data mining. However, it focuses on data mining of very large amounts of data, that is, data so large it does not fit in main memory. Because of the emphasis on size, many of our examples are about the Web or data derived from the Web.
The first edition was published by Cambridge University Press, and you get 20% discount by buying it here.
The second edition of the book will also be published soon. Jure Leskovec was added as a coauthor. There are three new chapters, on mining large graphs, dimensionality reduction, and machine learning.
There is a revised Chapter 2 that treats map-reduce programming in a manner closer to how it is used in practice, rather than how it was described in the original paper. Chapter 2 also has new material on algorithm design techniques for map-reduce.
Support Materials include Gradiance automated homeworks for the book and slides.
Thanks to authors agreement with the publisher, you can still download it free frominfolab.stanford.edu/~ullman/mmds.html .
The authors note if want to reuse parts of this book, you need to obtain their permission and acknowledge our authorship. They have seen evidence that other items they published have been appropriated and republished under other names, but that is easy to detect, as you will learn in Chapter 3.
