This paper is an introduction to stream data mining. Data streams are everywhere, from F1 racing over electricity networks to news feeds. Data stream mining relies on incremental algorithms that process streams under strict resource limitations. This paper focuses on, as well as extends the methods implemented in MOA (Java) and scikit-multiflow (Python), two open-source stream mining software suites currently being developed by the Machine Learning group at the University of Waikato. More information.
Heitor Murilo Gomes
I am currently a senior research fellow at the University of Waikato in the machine learning group. My main research area is Machine Learning, specially Evolving Data Streams, Concept Drift, Ensemble methods and Big Data Streams. I contribute to MOA (Java), StreamDM (Spark Streaming) and scikit-multiflow (Python) open data stream mining projects.