Machine Learning for Data Streams: with Practical Examples in MOA (Massive Online Analysis) (Albert Bifet, et al)

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Machine Learning for Data Streams: with Practical Examples in MOA (Massive Online Analysis) (Albert Bifet, et al)

Many information sources available today are so-called data streams, arriving sequentially and quickly. These sources include sensor networks, financial markets, social networks, and healthcare monitoring.

Real-time analysis is required, without the ability to save all of the data or even all of the incomplete data.

The algorithms and methods utilized in data stream mining and real-time analytics are presented in this book. After reading the explanations, readers can practice the procedures because the book takes a practical approach and uses MOA (Massive Online Analysis), a well-liked, publicly downloadable open-source software framework.

The book begins with a succinct introduction to the subject, going through massive data mining, fundamental techniques for mining data streams, and a straightforward MOA example. The chapters on sketching approaches, change, classification, ensemble methods, regression, clustering, and frequent pattern mining provide more in-depth treatments.

Ebook Details

About the Authors
  • Professor of Computer Science at Télécom ParisTech is Albert Bifet
  • Computer science professor Ricard Gavaldà teaches at Barcelona's Politècnica de Catalunya.
  • Professor and Dean of Computing at Hamilton, New Zealand's University of Waikato Geoff Holmes.
  • The professor of computer science at the University of Auckland in New Zealand is Bernhard Pfahringer.
Published Date / Year
(March 2, 2018)
288 pages
eBook Format

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