Machine Learning for Data Streams

with Practical Examples in MOA

Nonfiction, Computers, Advanced Computing, Artificial Intelligence, Database Management, General Computing
Cover of the book Machine Learning for Data Streams by Albert Bifet, Ricard Gavaldà, Geoff Holmes, Bernhard Pfahringer, The MIT Press
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Albert Bifet, Ricard Gavaldà, Geoff Holmes, Bernhard Pfahringer ISBN: 9780262346054
Publisher: The MIT Press Publication: March 9, 2018
Imprint: The MIT Press Language: English
Author: Albert Bifet, Ricard Gavaldà, Geoff Holmes, Bernhard Pfahringer
ISBN: 9780262346054
Publisher: The MIT Press
Publication: March 9, 2018
Imprint: The MIT Press
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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 books from The MIT Press

Cover of the book Working Minds by Albert Bifet, Ricard Gavaldà, Geoff Holmes, Bernhard Pfahringer
Cover of the book Something for Nothing by Albert Bifet, Ricard Gavaldà, Geoff Holmes, Bernhard Pfahringer
Cover of the book Globalizing Innovation by Albert Bifet, Ricard Gavaldà, Geoff Holmes, Bernhard Pfahringer
Cover of the book Spaceflight by Albert Bifet, Ricard Gavaldà, Geoff Holmes, Bernhard Pfahringer
Cover of the book The Sound of Innovation by Albert Bifet, Ricard Gavaldà, Geoff Holmes, Bernhard Pfahringer
Cover of the book Streetlights and Shadows by Albert Bifet, Ricard Gavaldà, Geoff Holmes, Bernhard Pfahringer
Cover of the book Principles of Commodity Economics and Finance by Albert Bifet, Ricard Gavaldà, Geoff Holmes, Bernhard Pfahringer
Cover of the book Technology in America by Albert Bifet, Ricard Gavaldà, Geoff Holmes, Bernhard Pfahringer
Cover of the book Touch by Albert Bifet, Ricard Gavaldà, Geoff Holmes, Bernhard Pfahringer
Cover of the book Disconnected by Albert Bifet, Ricard Gavaldà, Geoff Holmes, Bernhard Pfahringer
Cover of the book The Road to Democracy in Iran by Albert Bifet, Ricard Gavaldà, Geoff Holmes, Bernhard Pfahringer
Cover of the book Remarks on the Phonological Evolution of Russian in Comparison with the Other Slavic Languages by Albert Bifet, Ricard Gavaldà, Geoff Holmes, Bernhard Pfahringer
Cover of the book Sherrie Levine by Albert Bifet, Ricard Gavaldà, Geoff Holmes, Bernhard Pfahringer
Cover of the book Frame Innovation by Albert Bifet, Ricard Gavaldà, Geoff Holmes, Bernhard Pfahringer
Cover of the book Understanding Beliefs by Albert Bifet, Ricard Gavaldà, Geoff Holmes, Bernhard Pfahringer
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy