Mining of Massive Datasets

    By

    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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    Number of Pages: 476
    Format(s): Hardback - ISBN: 9781107077232
    Publication Date:
    Listed in: Data capture & analysis, Data mining, Databases & the Web
    Publisher: CAMBRIDGE UNIVERSITY PRESS

    Books by Jeffrey David Ullman

    Hardback

    RRP: £52.00

    Sale Price: £46.40

    Save: £5.60 per copy!

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