Mining of massive datasets

書誌事項

Mining of massive datasets

Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman

Cambridge University Press, c2020

3rd ed

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注記

Previous edition: 2014

Includes bibliographical references and index

内容説明・目次

内容説明

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.

目次

  • 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.

「Nielsen BookData」 より

詳細情報

  • NII書誌ID(NCID)
    BB29695528
  • ISBN
    • 9781108476348
  • 出版国コード
    uk
  • タイトル言語コード
    eng
  • 本文言語コード
    eng
  • 出版地
    Cambridge
  • ページ数/冊数
    xi, 553 p.
  • 大きさ
    26 cm
  • 分類
  • 件名
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