Welcome to Book Summaries
I keep this page to review the books that I have read. I hope that by keeping the notes, my reading would result in better retention over the time and at some point in the future I can easily review them again.
Books
1. Mining of Massive Data Sets
Authors: Anand Rajaraman, Jeffrey D. Ullman
Google Book description: 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.
Here is the website of the book and also the course taught in Stanford http://www.mmds.org/.