Machine Learning for Data Streams: with Practical Examples in MOA (Adaptive Computation and Machine Learning series)

This book provides a comprehensive introduction to data stream mining and real-time analytics, with a hands-on approach using MOA, a popular open-source software framework.

The author covers big data mining, basic methodologies for mining data streams, and a simple example of MOA.

The book then delves into various techniques such as sketching, change detection, classification, ensemble methods, regression, clustering, and frequent pattern mining.

Each chapter includes exercises, an MOA-based lab session, or both, allowing readers to try out the techniques after reading the explanations.

Additionally, the book discusses the MOA software, covering its graphical user interface, command line, API, and development of new methods within MOA.

This book is an essential reference for those 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.