Description
Machine Learning for Data Streams
with Practical Examples in MOA
Authors: Bifet Albert, Gavaldà Ricard, Holmes Geoff, Pfahringer Bernhard, Bach Francis
Language: EnglishSubject for Machine Learning for Data Streams:
Approximative price 60.23 €
In Print (Delivery period: 14 days).
Add to cart the book of Bifet Albert, Gavaldà Ricard, Holmes Geoff, Pfahringer Bernhard, Bach Francis
Publication date: 04-2018
262 p. · 17.9x23.8 cm · Hardback
262 p. · 17.9x23.8 cm · Hardback
Description
/li>Biography
/li>
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.
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.
Albert Bifet< is Professor of Computer Science at Télécom ParisTech.
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