Machine Learning for Data Streams

<b>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.</b><p>Today many information sources&#151;including sensor networks financial markets social networks and healthcare monitoring&#151;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.</p><p>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.</p>