Jure Leskovec, Anand Rajaraman, Jeffrey David UllmanCambridge University PressEdition: 3, 1/9/2020EAN 9781108476348, ISBN10: 1108476341Hardcover, 565 pages, 25.4 x 17.8 x 2.8 cmLanguage: EnglishWritten 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 mining2. MapReduce and the new software stack3. Finding similar items4. Mining data streams5. Link analysis6. Frequent itemsets7. Clustering8. Advertising on the web9. Recommendation systems10. Mining social-network graphs11. Dimensionality reduction12. Large-scale machine learning13. Neural nets and deep learningIndex.
