{"product_id":"machine-learning-for-data-streams-isbn-9780262547833","title":"Machine Learning for Data Streams","description":"\u003cb\u003eA 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.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eToday 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.\u003cbr\u003e\u003cbr\u003eThe 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.List of Figures xiii\u003cbr\u003eList of Tables xvii\u003cbr\u003ePreface xix\u003cbr\u003eI Introduction 1\u003cbr\u003e1 Introduction 3\u003cbr\u003e2 Big Data Stream Mining 11\u003cbr\u003e3 Hands-on Introduction to MOA 21\u003cbr\u003eII Stream Mining 33\u003cbr\u003e4 Streams and Sketches 35\u003cbr\u003e5 Dealing with Change 67\u003cbr\u003e6 Classification 85\u003cbr\u003e7 Ensemble Methods 129\u003cbr\u003e8 Regression 143\u003cbr\u003e9 Clustering 149\u003cbr\u003e10 Frequent Pattern Mining 165\u003cbr\u003eIII The MOA Software 185\u003cbr\u003e11 Introduction to MOA and Its Ecosystem 187\u003cbr\u003e12 The Graphical User Interface 201\u003cbr\u003e13 Using the Command Line 217\u003cbr\u003e14 Using the API\u003cbr\u003e15 Developing New Methods in MOA 227\u003cbr\u003eBibliography 239\u003cbr\u003eIndex 257Albert Bifet is Professor of Computer Science at Télécom ParisTech.\u003cbr\u003eRicard Gavaldà is Professor of Computer Science at the Politècnica de Catalunya, Barcelona.\u003cbr\u003eGeoff Holmes is Professor and Dean of Computing at the University of Waikato in Hamilton, New Zealand.\u003cbr\u003eBernhard Pfahringer is Professor of Computer Science at the University of Auckland, New Zealand.","brand":"The MIT Press","offers":[{"title":"Default Title","offer_id":46299824718053,"sku":"NP9780262547833","price":55.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780262547833.jpg?v=1767732012","url":"https:\/\/k12savings.com\/products\/machine-learning-for-data-streams-isbn-9780262547833","provider":"K12savings","version":"1.0","type":"link"}