Stream data mining : algorithms and their probabilistic properties / Leszek Rutkowski, Maciej Jaworski, Piotr Duda.

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Stream data mining : algorithms and their probabilistic properties / Leszek Rutkowski, Maciej Jaworski, Piotr Duda.

9783030139629 (electronic book)
303013962X (electronic book)
9783030139612
3030139611

https://doi.org/10.1007/978-3-030-13
https://doi.org/10.1007/978-3-030-13962-9

Cham, Switzerland : Springer, [2020]

English

1 online resource

10.1007/978-3-030-13
10.1007/978-3-030-13962-9 doi

QA76.9.D343 R88 2020

006.312

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 deal with stream data, e.g. in telecommunication, banking, and sensor networks.

Includes bibliographical references and index.

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Description based on online resource; title from digital title page (viewed on June 26, 2019).

Studies in big data ; v. 56.

Print version: 9783030139612

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