Mining of Massive Datasets / by J. Leskovec and others. [Electronic Resource]
Material type: Computer filePublication details: Cambridge : Cambridge University Press, 2014Edition: 2nd EdDescription: xii, 467pISBN:- 9781139924801
- 006.312Â L563M
Item type | Home library | Collection | Call number | Status | Notes | Date due | Barcode | Item holds | |
---|---|---|---|---|---|---|---|---|---|
e-Book | S. R. Ranganathan Learning Hub Online | Textbook | 006.312 L563M (Browse shelf(Opens below)) | Available (e-Book For Access) | Platform : Cambridge Core | EB0364 |
Browsing S. R. Ranganathan Learning Hub shelves, Shelving location: Online, Collection: Textbook Close shelf browser (Hides shelf browser)
006.312 At82D Data Mining for the Social Sciences : An Introduction | 006.312 K28D Data Science | 006.312 K849D Data Science : Concepts and Practice | 006.312 L563M Mining of Massive Datasets | 006.312 M364E Ethics of Data and Analytics : Concepts and Cases | 006.312 T618R R for Data Science | 006.312 W631R R for Data Science : Import, Tidy, Transform, Visualize and Model Data |
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.
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