Overview
- Presents a unique and innovative 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
- Is intended for a professional audience composed of researchers and practitioners who deal with stream data (e.g. in telecommunication, banking, and sensor networks)
Part of the book series: Studies in Big Data (SBD, volume 56)
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Table of contents (15 chapters)
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Front Matter
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Data Stream Mining
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Front Matter
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Splitting Criteria in Decision Trees for Data Stream Mining
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Front Matter
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Probabilistic Neural Networks for Data Stream Mining
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Front Matter
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Keywords
About this book
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.
Authors and Affiliations
Bibliographic Information
Book Title: Stream Data Mining: Algorithms and Their Probabilistic Properties
Authors: Leszek Rutkowski, Maciej Jaworski, Piotr Duda
Series Title: Studies in Big Data
DOI: https://doi.org/10.1007/978-3-030-13962-9
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-13961-2Published: 26 March 2019
eBook ISBN: 978-3-030-13962-9Published: 16 March 2019
Series ISSN: 2197-6503
Series E-ISSN: 2197-6511
Edition Number: 1
Number of Pages: IX, 330
Number of Illustrations: 48 b/w illustrations, 63 illustrations in colour
Topics: Computational Intelligence, Data Mining and Knowledge Discovery, Signal, Image and Speech Processing, Big Data/Analytics, Artificial Intelligence