Mining of Massive Datasets

Now in its third edition, this book focuses on practical algorithms for mining data from even the largest datasets.

Jure Leskovec (Author), Anand Rajaraman (Author), Jeffrey David Ullman (Author)

9781108476348, Cambridge University Press

Hardback, published 9 January 2020

565 pages, 76 b/w illus. 250 exercises
25.3 x 17.8 x 2.8 cm, 1.24 kg

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 MapReduce 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 third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs.

1. Data mining
2. MapReduce and the new software stack
3. Finding similar items
4. Mining data streams
5. Link analysis
6. Frequent itemsets
7. Clustering
8. Advertising on the web
9. Recommendation systems
10. Mining social-network graphs
11. Dimensionality reduction
12. Large-scale machine learning
13. Neural nets and deep learning
Index.

Subject Areas: Pattern recognition [UYQP], Machine learning [UYQM], Data mining [UNF], Databases [UN], Knowledge management [KJMV3], Information theory [GPF]