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  3. Mining of Massive Datasets
  • Cited by 779
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    • Publisher:
      Cambridge University Press
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      October 2011
      ISBN:
      DOI:
      https://doi.org/10.1017/CBO9781139058452
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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

    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 which can be used on 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. The PageRank idea and related tricks for organizing the Web are covered next. Other chapters cover the problems of finding frequent itemsets and clustering. The final chapters cover two applications: recommendation systems and Web advertising, each vital in e-commerce. Written by two authorities in database and Web technologies, this book is essential reading for students and practitioners alike.

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