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  3. Mining of Massive Datasets
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    • This textbook will transition to Cambridge Core on 6 July 2026. Please take a moment to export any annotations you may have created on the eTextbook before the move.
      • 3rd edition
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    • Publisher:
      Cambridge University Press
      Publication date:
      16 April 2020
      09 January 2020
      ISBN:
      9781108684163
      9781108476348
      DOI:
      https://doi.org/10.1017/9781108684163
      Dimensions:
      (244 x 170 mm)
      Weight & Pages:
      1.24kg, 566 Pages
      Dimensions:
      Weight & Pages:
      • Subjects:
      Knowledge Management, Databases and Data Mining, Computer Science, Pattern Recognition and Machine Learning

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    Subjects:
    Knowledge Management, Databases and Data Mining, Computer Science, Pattern Recognition and Machine Learning
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    Book description

    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.

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