Details

Stream Data Mining: Algorithms and Their Probabilistic Properties


Stream Data Mining: Algorithms and Their Probabilistic Properties


Studies in Big Data, Band 56

von: Leszek Rutkowski, Maciej Jaworski, Piotr Duda

160,49 €

Verlag: Springer
Format: PDF
Veröffentl.: 16.03.2019
ISBN/EAN: 9783030139629
Sprache: englisch

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Beschreibungen

<p></p><p>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.</p><p></p>
Introduction and Overview of the Main Results of the Book.-&nbsp;Basic concepts of data stream mining.-&nbsp;&nbsp;Decision Trees in Data Stream Mining.-&nbsp;&nbsp;Splitting Criteria based on the McDiarmid’s Theorem.
<p></p><p>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. </p><br><p></p>
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)
Presents a unique approach to stream data mining <br><br>Contrary to the vast majority of previous approaches, mainly based on some heuristics, this book shows methods and algorithms which are mathematically justified<br><br>Designed for a professional audience composed of researchers and practitioners dealing with stream data (telecommunication, banking, sensor networks)<br><br>

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