160,39 €
188,69 €
-15% su kodu: ENG15
Mining of Massive Datasets
160,39 € 
- Išsiųsime per 10–14 d.d.
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 impo…
160.39
2025-08-10 23:59:00
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- Autorius: Jure Leskovec
- Leidėjas: Cambridge University Press
- ISBN-10: 1108476341
- ISBN-13: 9781108476348
- Formatas: 18.3 x 24.9 x 2.8 cm, kieti viršeliai
- Kalba: Anglų
- Extra -15 % nuolaida šiai knygai su kodu: ENG15
Atsiliepimai
Aprašymas
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.EXTRA 15 % nuolaida su kodu: ENG15
160,39 €
188,69 €
Išsiųsime per 10–14 d.d.
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- Autorius: Jure Leskovec
- Leidėjas: Cambridge University Press
- ISBN-10: 1108476341
- ISBN-13: 9781108476348
- Formatas: 18.3 x 24.9 x 2.8 cm, kieti viršeliai
- Kalba: Anglų
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.
Atsiliepimai