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Stream data miningalgorithms and the...

Duda, Piotr.

Stream data miningalgorithms and their probabilistic properties /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Stream data miningby Leszek Rutkowski, Maciej Jaworski, Piotr Duda.
Reminder of title:	algorithms and their probabilistic properties /
Author:	Rutkowski, Leszek.
other author:	Jaworski, Maciej.
Published:	Cham :Springer International Publishing :2020.
Description:	ix, 330 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Data mining.
Online resource:	https://doi.org/10.1007/978-3-030-13962-9
ISBN:	9783030139629$q(electronic bk.)

Stream data miningalgorithms 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.1..

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$q(electronic bk.)

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

184440
Data mining.

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

Dewey Class. No.: 006.312

Stream data miningalgorithms and their probabilistic properties /
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505 0 $a 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.
520 $a 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.
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