Stream Data Mining: Algorithms and Their Probabilistic Properties
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Beschreibung
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.
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.
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
Contrary to the vast majority of previous approaches, mainly based on some heuristics, this book shows methods and algorithms which are mathematically justified
Designed for a professional audience composed of researchers and practitioners dealing with stream data (telecommunication, banking, sensor networks)
Autor*in
Leszek Rutkowski
Themen in »Stream Data Mining: Algorithms and Their Probabilistic Properties«
Big Data Data Science Stream Data Mining Streaming Stream Data Algorithms