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Machine Learning for Data Streams: Practical Examples in MOA (Massive Online Analysis)
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  • Title: Machine Learning for Data Streams: with Practical Examples in MOA (Massive Online Analysis)
  • Author(s) Albert Bifet, Ricard Gavalda, Geoff Holmes, Bernhard Pfahringer
  • Publisher: The MIT Press (March 2, 2018)
  • Hardcover: 288 pages
  • eBook: HTML
  • Language: English
  • ISBN-10: 0262037793
  • ISBN-13: 978-0262037792
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Book Description

Today many information sources - including sensor networks, financial markets, social networks, and healthcare monitoring - are so-called data streams, arriving sequentially and at high speed. Analysis must take place in real time, with partial data and without the capacity to store the entire data set.

This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available open-source software framework, allowing readers to try out the techniques after reading the explanations.

The book first offers a brief introduction to the topic, covering big data mining, basic methodologies for mining data streams, and a simple example of MOA. More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining.

About the Authors
  • Albert Bifet is Professor of Computer Science at Telecom ParisTech.
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