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Data Mining

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Handbook on Data Management in Information Systems

Part of the book series: International Handbooks on Information Systems ((INFOSYS))

Abstract

Data mining, also referred to as database mining or knowledge discovery in databases (KDD), is a new research area that aims at the discovery of useful information from large datasets. Data mining uses statistical analysis and inference to extract interesting trends and events, create useful reports, support decision making,etc. It exploits the massive amounts of data to achieve business, operational or scientific goals.

In this chapter we give an overview of the data mining process and we describe the fundamental data mining problems: mining association rules and sequential patterns, classification and prediction, and clustering. Basic algorithms developed to efficiently process data mining tasks are discussed and illustrated with examples of their operation on real data sets.

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  1. Institute of Computing Science, Poznań University of Technology, Poznań, Poland

    Tadeusz Morzy & Maciej Zakrzewicz

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  1. Tadeusz Morzy
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  1. Institute of Bioorganic Chemistry, Polish Academy of Sciences, ul. Noskowskiego 12, 61-704, Poznań, Poland

    Jacek Błażewicz

  2. Faculty of Business Administration, Memorial University of Newfoundland, NF A1B 3X5, St. John’s, Canada

    Wieslaw Kubiak

  3. Institute of Computing Science, Poznań University of Technology, ul. Piotrowo 3a, 60-965, Poznań, Poland

    Tadeusz Morzy

  4. Information and Computer Science Laboratory, Telcordia Technologies, 445 South Street MCC-1J346B, 07960, Morristown, NJ, USA

    Marek Rusinkiewicz

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Morzy, T., Zakrzewicz, M. (2003). Data Mining. In: Błażewicz, J., Kubiak, W., Morzy, T., Rusinkiewicz, M. (eds) Handbook on Data Management in Information Systems. International Handbooks on Information Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24742-5_11

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