Machine Learning for Data Streams

with Practical Examples in MOA

Albert Bifet, Ricard Gavalda, Geoffrey Holmes , Bernhard Pfahringer

$135

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English

MIT Press
09 May 2023

Data mining; Artificial intelligence

Series: Adaptive Computation and Machine Learning series

A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework.

A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework.

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. Most of these chapters include exercises, an MOA-based lab session, or both. Finally, the book discusses the MOA software, covering the MOA graphical user interface, the command line, use of its API, and the development of new methods within MOA. The book will be an essential reference for readers who want to use data stream mining as a tool, researchers in innovation or data stream mining, and programmers who want to create new algorithms for MOA.

By: Albert Bifet, Ricard Gavalda, Geoffrey Holmes, Bernhard Pfahringer
Imprint: MIT Press
Country of Publication: United States
Dimensions: Height: 229mm, Width: 178mm,
Weight: 369g
ISBN: 9780262547833
ISBN 10: 026254783X
Series: Adaptive Computation and Machine Learning series
Pages: 288
Publication Date: 09 May 2023
Audience: General/trade , ELT Advanced
Format: Paperback
Publisher's Status: Active

Albert Bifet is Professor of Computer Science at Telecom ParisTech. Ricard Gavald is Professor of Computer Science at the Polit cnica de Catalunya, Barcelona. Geoff Holmes is Professor and Dean of Computing at the University of Waikato in Hamilton, New Zealand. Bernhard Pfahringer is Professor of Computer Science at the University of Auckland, New Zealand.