J. Gama. Knowledge Discovery from Data Streams. Chapman & Hall/CRC, 2010.

Stream mining was one of three parts of the "Data Mining Algorithms II" Lecture held by Prof. Seidl at RWTH Aachen University: high-dimensional data, stream data and graph data. While not resource (to the best of my knowledge) does cover all discussed algorithms, Gama offers a well-structured introduction to the basics of stream mining. Covered algorithms and concepts - also important for the lecture - are count and sliding window methods (for example counting the number of occurrences or distinct values, or creating a hot-list). Further, change detection, histograms, clustering and decision trees on stream data are discussed.

What is your opinion on the summarized work? Or do you know related work that is of interest? Let me know your thoughts in the comments below: