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Basic Concepts of Data Stream Mining

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Stream Data Mining: Algorithms and Their Probabilistic Properties

Part of the book series: Studies in Big Data ((SBD,volume 56))

Abstract

Data stream mining, as its name suggests, is connected with two basic fields of computer science, i.e. data mining and data streams. Data mining [1,2,3,4] is an interdisciplinary subfield of computer science whose main aim is to develop tools and methods for exploring knowledge from large datasets. Data mining is strictly related to statistics, pattern recognition [5] and machine learning [6], using methods like neural networks, decision trees, Bayesian networks or support vector machines. Neural networks are often considered a method belonging to soft computing or computational intelligence [7]. Another soft computing concept used in data mining is fuzzy logic. It should be noted that all mentioned above subfields of computer science are not strictly defined and overlap in many issues. Moreover, data mining is something more than learning or extracting knowledge, and includes, among others, database systems or data visualization as well. However, this book focuses mainly on the learning aspect of data mining.

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Authors and Affiliations

  1. Institute of Computational Intelligence, Czestochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski, Maciej Jaworski & Piotr Duda

  2. Information Technology Institute, University of Social Sciences, Lodz, Poland

    Leszek Rutkowski

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  1. Leszek Rutkowski
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  2. Maciej Jaworski
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  3. Piotr Duda
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Correspondence to Leszek Rutkowski .

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Rutkowski, L., Jaworski, M., Duda, P. (2020). Basic Concepts of Data Stream Mining . In: Stream Data Mining: Algorithms and Their Probabilistic Properties. Studies in Big Data, vol 56. Springer, Cham. https://doi.org/10.1007/978-3-030-13962-9_2

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