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Stream data mining = algorithms and ...

Rutkowski, Leszek.

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Stream data mining = algorithms and their probabilistic properties /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Stream data mining/ by Leszek Rutkowski, Maciej Jaworski, Piotr Duda.
其他題名:	algorithms and their probabilistic properties /
作者:	Rutkowski, Leszek.
其他作者:	Jaworski, Maciej.
出版者:	Cham :Springer International Publishing : : 2020.,
面頁冊數:	ix, 330 p. :ill., digital ;24 cm.
內容註:	Introduction and Overview of the Main Results of the Book -- Basic concepts of data stream mining -- Decision Trees in Data Stream Mining -- Splitting Criteria based on the McDiarmid's Theorem.
Contained By:	Springer eBooks
標題:	Data mining. -
電子資源:	https://doi.org/10.1007/978-3-030-13962-9
ISBN:	9783030139629

Stream data mining = algorithms and their probabilistic properties /
Rutkowski, Leszek.

Stream data miningalgorithms and their probabilistic properties /[electronic resource] :by Leszek Rutkowski, Maciej Jaworski, Piotr Duda. - Cham :Springer International Publishing :2020. - ix, 330 p. :ill., digital ;24 cm. - Studies in big data,v.562197-6503 ;. - Studies in big data ;v.56..

Introduction and Overview of the Main Results of the Book -- Basic concepts of data stream mining -- Decision Trees in Data Stream Mining -- Splitting Criteria based on the McDiarmid's Theorem.

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.

ISBN: 9783030139629

Standard No.: 10.1007/978-3-030-13962-9doiSubjects--Topical Terms:

562972
Data mining.

LC Class. No.: QA76.9.D343 / R88 2020

Dewey Class. No.: 006.312

Stream data mining = algorithms and their probabilistic properties /
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