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Machine Learning for Data Streams: with Practical Examples
in MOA (Adaptive Computation and Machine Learning
series)
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Machine Learning for Data Streams: with Practical Examples
in MOA (Adaptive Computation and Machine Learning
series)
BOOK DETAIL
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Series: Adaptive Computation and Machine Learning series Hardcover: 288 pages Publisher: The MIT Press
(March 2, 2018) Language: English ISBN-10: 0262037793 ISBN-13: 978-0262037792 Product Dimensions:
9.1 x 7.2 x 1 inches
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Book Description
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.

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