Mining of massive datasets / Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman, Standford University.
Leskovec, Jurij| Call Number | 006.312 |
| Author | Leskovec, Jurij, author. |
| Title | Mining of massive datasets / Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman, Standford University. |
| Edition | Second edition. |
| Physical Description | 1 online resource (xi, 467 pages) : digital, PDF file(s). |
| Notes | Title from publisher's bibliographic system (viewed on 05 Oct 2015). |
| Contents | Data mining -- MapReduce and the new software stack -- Finding similar items -- Mining data streams -- Link analysis -- Frequent itemsets -- Clustering -- Advertising on the Web -- Recommendation systems -- Mining social-network graphs -- Dimensionality reduction -- Large-scale machine learning. |
| Summary | 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. |
| Added Author | Rajaraman, Anand, author. Ullman, Jeffrey D., 1942- author. |
| Subject | DATA MINING. BIG DATA. |
| Multimedia |
Total Ratings:
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| Summary | 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. |
| Notes | Title from publisher's bibliographic system (viewed on 05 Oct 2015). |
| Contents | Data mining -- MapReduce and the new software stack -- Finding similar items -- Mining data streams -- Link analysis -- Frequent itemsets -- Clustering -- Advertising on the Web -- Recommendation systems -- Mining social-network graphs -- Dimensionality reduction -- Large-scale machine learning. |
| Subject | DATA MINING. BIG DATA. |
| Multimedia |