Knowledge Discovery from Data Streams Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

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.

Knowledge Discovery from Data Streams. Introduction to Data Streams. Change Detection. Maintaining Histograms from Data Streams. Evaluating Streaming Algorithms. Clustering from Data Streams. Frequent Pattern Mining. Decision Trees from Data Streams. Novelty Detection in Data Streams. Ensembles of Classifiers. Time Series Data Streams. Ubiquitous Data Mining. Final Comments. Appendix. Bibliography. Index.

João Gama is an associate professor and senior researcher in the Laboratory of Artificial Intelligence and Decision Support (LIAAD) at the University of Porto in Portugal.