Machine Learning for Data Streams
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-c...
I tiakina i:
| Ngā kaituhi matua: | , , , |
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| Hōputu: | Online |
| Reo: | Ingarihi |
| I whakaputaina: |
The MIT Press
2022
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| Ngā marau: | |
| Urunga tuihono: | ONIX_20220221_9780262346047_74 |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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| _version_ | 1869523615397969920 |
|---|---|
| author | Bifet, Albert Gavaldà, Ricard Holmes, Geoff Pfahringer, Bernhard |
| author_browse | Bifet, Albert Gavaldà, Ricard Holmes, Geoff Pfahringer, Bernhard |
| author_facet | Bifet, Albert Gavaldà, Ricard Holmes, Geoff Pfahringer, Bernhard |
| author_sort | Bifet, Albert |
| collection | Directory of Open Access Books |
| 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. |
| format | Online |
| id | doab-20.500.12854ir-78554 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | The MIT Press |
| publisherStr | The MIT Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-785542024-04-14T10:27:50Z Machine Learning for Data Streams Bifet, Albert Gavaldà, Ricard Holmes, Geoff Pfahringer, Bernhard data mining stream data mining statistics techniques analysis learning extract algorithm data stream MOA massive online analysis software implementation applications approximation big data thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning 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. 2022-02-21T15:11:35Z 2022-02-21T15:11:35Z 2018 book ONIX_20220221_9780262346047_74 9780262346047 9780262037792 https://directory.doabooks.org/handle/20.500.12854/78554 eng Adaptive Computation and Machine Learning series image/jpeg n/a https://doi.org/10.7551/mitpress/10654.001.0001 The MIT Press The MIT Press 10.7551/mitpress/10654.001.0001 10.7551/mitpress/10654.001.0001 ae0cf962-f685-4933-93d1-916defa5123d 9780262346047 9780262037792 The MIT Press 288 Cambridge open access |
| spellingShingle | data mining stream data mining statistics techniques analysis learning extract algorithm data stream MOA massive online analysis software implementation applications approximation big data thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning Bifet, Albert Gavaldà, Ricard Holmes, Geoff Pfahringer, Bernhard Machine Learning for Data Streams |
| title | Machine Learning for Data Streams |
| title_full | Machine Learning for Data Streams |
| title_fullStr | Machine Learning for Data Streams |
| title_full_unstemmed | Machine Learning for Data Streams |
| title_short | Machine Learning for Data Streams |
| title_sort | machine learning for data streams |
| topic | data mining stream data mining statistics techniques analysis learning extract algorithm data stream MOA massive online analysis software implementation applications approximation big data thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning |
| topic_facet | data mining stream data mining statistics techniques analysis learning extract algorithm data stream MOA massive online analysis software implementation applications approximation big data thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning |
| url | ONIX_20220221_9780262346047_74 |
| work_keys_str_mv | AT bifetalbert machinelearningfordatastreams AT gavaldaricard machinelearningfordatastreams AT holmesgeoff machinelearningfordatastreams AT pfahringerbernhard machinelearningfordatastreams |