• https://www.crcpress.com/Knowledge-Discovery-from-Data-Streams/Gama/9781439826119?source=igodigital
• http://www.liaad.up.pt/area/jgama/DataStreamsCRC.pdf
  

• Covers the most relevant topics in learning from data streams, including histograms, clustering, frequent patterns, decision trees, time series, and novelty detection
• Explores advanced areas, such as ubiquitous data stream mining
• Includes pseudo-code of more than 30 streaming-like algorithms
• Contains many illustrative and pedagogical examples of applications, such as network monitoring, web mining, sensor networks, telecommunications, and data management
  

Since the beginning of the Internet age and the increased use of ubiquitous computing devices, the large volume and continuous flow of distributed data have imposed new constraints on the design of learning algorithms. Exploring how to extract knowledge structures from evolving and time-changing data, Knowledge Discovery from Data Streams presents a coherent overview of state-of-the-art research in learning from data streams.

The book covers the fundamentals that are imperative to understanding data streams and describes important applications, such as TCP/IP traffic, GPS data, sensor networks, and customer click streams. It also addresses several challenges of data mining in the future, when stream mining will be at the core of many applications. These challenges involve designing useful and efficient data mining solutions applicable to real-world problems. In the appendix, the author includes examples of publicly available software and online data sets.

This practical, up-to-date book focuses on the new requirements of the next generation of data mining. Although the concepts presented in the text are mainly about data streams, they also are valid for different areas of machine learning and data mining.