ABSTRACT
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
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.
