Mining of Massive Datasets
Friday, March 9, 2012
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.
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