
Machine Learning for Data Streams
with Practical Examples in MOA
MIT Press
Published on 2. March 2018
Book
Hardback
288 pages
978-0-262-03779-2 (ISBN)
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.
More details
Series
Language
English
Place of publication
Cambridge
United States
Publishing group
MIT Press Ltd
Target group
Professional and scholarly
Interest Age: From 18 years
Product notice
Cloth over boards
Illustrations
29 s/w Abbildungen, 21 farbige Abbildungen
21 color illus., 29 b&w illus.
Dimensions
Height: 229 mm
Width: 178 mm
Thickness: 24 mm
ISBN-13
978-0-262-03779-2 (9780262037792)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Albert Bifet | Ricard Gavalda | Geoffrey Holmes
Machine Learning for Data Streams
with Practical Examples in MOA
E-Book
03/2018
MIT Press
€53.99
Available for download
Persons
Author
Professor of Computer ScienceTelecom ParisTech
ProfessorUniversitat Politecnica de Catalunya, Campus Nord
Professor and Dean of Computing and Mathematical SciencesUniversity of Waikato
ProfessorUniversity of Auckland