Stream Data Mining: Algorithms and Their Probabilistic Properties

von Leszek Rutkowski, Maciej Jaworski, Piotr Duda

330 Seiten, Hardcover

Kurzbeschreibung des Verlags

This book presents a unique approach to stream data mining. Unlike the vast majority of previous approaches, which are largely based on heuristics, it highlights methods and algorithms that are mathematically justified. First, it describes how to adapt static decision trees to accommodate data streams; in this regard, new splitting criteria are developed to guarantee that they are asymptotically equivalent to the classical batch tree. Moreover, new decision trees are designed, leading to the original concept of hybrid trees. In turn, nonparametric techniques based on Parzen kernels and orthogonal series are employed to address concept drift in the problem of non-stationary regressions and classification in a time-varying environment. Lastly, an extremely challenging problem that involves designing ensembles and automatically choosing their sizes is described and solved. Given its scope, the book is intended for a professional audience of researchers and practitioners who dealwith stream data, e.g. in telecommunication, banking, and sensor networks.

• Reihe: Studies in Big Data
• Themen: Informatik und Informationstechnologie Informatik Künstliche Intelligenz (KI)
• ISBN: 9783030139612
• Sprache: Englisch
• Erscheinungsdatum: 26.03.2019
• Größe: 23.5 x 15.5 cm
• Verlag: Springer International Publishing