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Spatio-temporal clustering

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Data Mining and Knowledge Discovery Handbook

Summary

Spatio-temporal clustering is a process of grouping objects based on their spatial and temporal similarity. It is relatively new subfield of data mining which gained high popularity especially in geographic information sciences due to the pervasiveness of all kinds of location-based or environmental devices that record position, time or/and environmental properties of an object or set of objects in real-time. As a consequence, different types and large amounts of spatio-temporal data became available that introduce new challenges to data analysis and require novel approaches to knowledge discovery. In this chapter we concentrate on the spatio-temporal clustering in geographic space. First, we provide a classification of different types of spatio-temporal data. Then, we focus on one type of spatio-temporal clustering - trajectory clustering, provide an overview of the state-of-the-art approaches and methods of spatio-temporal clustering and finally present several scenarios in different application domains such as movement, cellular networks and environmental studies.

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Author information

Authors and Affiliations

  1. University of Konstanz, Konstanz, Germany

    Slava Kisilevich & Florian Mansmann

  2. Institute of Information Science and Technologies, Pisa, Italy

    Mirco Nanni

  3. University of Pisa, Pisa, Italy

    Salvatore Rinzivillo

Authors
  1. Slava Kisilevich
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  2. Florian Mansmann
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  3. Mirco Nanni
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  4. Salvatore Rinzivillo
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Corresponding author

Correspondence to Slava Kisilevich .

Editor information

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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Kisilevich, S., Mansmann, F., Nanni, M., Rinzivillo, S. (2009). Spatio-temporal clustering. 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_44

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  • DOI: https://doi.org/10.1007/978-0-387-09823-4_44

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  • Print ISBN: 978-0-387-09822-7

  • Online ISBN: 978-0-387-09823-4

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