Mining of Massive Datasets, 2nd Edition

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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.

Table of Contents

Chapter 1 Data Mining
Chapter 2 Mapreduce And The New Software Stack
Chapter 3 Finding Similar Items
Chapter 4 Mining Data Streams
Chapter 5 Link Analysis
Chapter 6 Frequent Itemsets
Chapter 7 Clustering
Chapter 8 Advertising On The Web
Chapter 9 Recommendation Systems
Chapter 10 Mining Social-Network Graphs
Chapter 11 Dimensionality Reduction
Chapter 12 Large-Scale Machine Learning

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