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  3. Mining of Massive Datasets
  • Cited by 686
      • 2nd edition
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    • Publisher:
      Cambridge University Press
      Publication date:
      December 2014
      November 2014
      ISBN:
      9781139924801
      DOI:
      https://doi.org/10.1017/CBO9781139924801
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      • Subjects:
      Knowledge Management, Databases and Data Mining, Computer Science, Computational Statistics, Machine Learning and Information Science, Statistics and Probability
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    Subjects:
    Knowledge Management, Databases and Data Mining, Computer Science, Computational Statistics, Machine Learning and Information Science, Statistics and Probability
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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 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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