Stream Data Mining: Algorithms and Their Probabilistic Properties
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 st
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Leszek Rutkowski Maciej Jaworski Piotr Duda
Stream Data Mining: Algorithms and Their Probabilistic Properties
Studies in Big Data Volume 56
Series Editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
The series “Studies in Big Data” (SBD) publishes new developments and advances in the various areas of Big Data- quickly and with a high quality. The intent is to cover the theory, research, development, and applications of Big Data, as embedded in the fields of engineering, computer science, physics, economics and life sciences. The books of the series refer to the analysis and understanding of large, complex, and/or distributed data sets generated from recent digital sources coming from sensors or other physical instruments as well as simulations, crowd sourcing, social networks or other internet transactions, such as emails or video click streams and other. The series contains monographs, lecture notes and edited volumes in Big Data spanning the areas of computational intelligence incl. neural networks, evolutionary computation, soft computing, fuzzy systems, as well as artificial intelligence, data mining, modern statistics and Operations research, as well as self-organizing systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output. ** Indexing: The books of this series are submitted to ISI Web of Science, DBLP, Ulrichs, MathSciNet, Current Mathematical Publications, Mathematical Reviews, Zentralblatt Math: MetaPress and Springerlink.
More information about this series at http://www.springer.com/series/11970
Leszek Rutkowski Maciej Jaworski Piotr Duda •
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Stream Data Mining: Algorithms and Their Probabilistic Properties
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Leszek Rutkowski Institute of Computational Intelligence Czestochowa University of Technology Częstochowa, Poland
Maciej Jaworski Institute of Computational Intelligence Czestochowa University of Technology Częstochowa, Poland
Information Technology Institute University of Social Sciences Lodz, Poland Piotr Duda Institute of Computational Intelligence Czestochowa University of Technology Częstochowa, Poland
ISSN 2197-6503 ISSN 2197-6511 (electronic) Studies in Big Data ISBN 978-3-030-13961-2 ISBN 978-3-030-13962-9 (eBook) https://doi.org/10.1007/978-3-030-13962-9 Library of Congress Control Number: 2019931869 © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply,
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