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Data Mining and Knowledge Discovery Handbook pp 759–787Cite as

Data Stream Mining

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Abstract

Data mining is concerned with the process of computationally extracting hidden knowledge structures represented in models and patterns from large data repositories. It is an interdisciplinary field of study that has its roots in databases, statistics, machine learning, and data visualization. Data mining has emerged as a direct outcome of the data explosion that resulted from the success in database and data warehousing technologies over the past two decades (Fayyad, 1997,Fayyad, 1998,Kantardzic, 2003).

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Authors and Affiliations

  1. Centre for Distributed Systems and Software Engineering, Monash University, Victoria, 3800, Australia

    Mohamed Medhat Gaber, Arkady Zaslavsky & Shonali Krishnaswamy

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  1. Mohamed Medhat Gaber
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  2. Arkady Zaslavsky
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  3. Shonali Krishnaswamy
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Correspondence to Shonali Krishnaswamy .

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Editors and Affiliations

  1. , Dept. Industrial Engineering, Tel Aviv University, Ramat Aviv, 69978, Israel

    Oded Maimon

  2. , Dept. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel

    Lior Rokach

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Gaber, M.M., Zaslavsky, A., Krishnaswamy, S. (2009). Data Stream Mining. In: Maimon, O., Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09823-4_39

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  • DOI: https://doi.org/10.1007/978-0-387-09823-4_39

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