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Spatiotemporal Pattern Mining: Algorithms and Applications

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Frequent Pattern Mining

Abstract

With the fast development of positioning technology, spatiotemporal data has become widely available nowadays. Mining patterns from spatiotemporal data has many important applications in human mobility understanding, smart transportation, urban planning and ecological studies. In this chapter, we provide an overview of spatiotemporal data mining methods. We classify the patterns into three categories: (1) individual periodic pattern; (2) pairwise movement pattern and (3) aggregative patterns over multiple trajectories. This chapter states the challenges of pattern discovery, reviews the state-of-the-art methods and also discusses the limitations of existing methods.

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Authors and Affiliations

  1. Pennsylvania State University, University Park, USA

    Zhenhui Li

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  1. Zhenhui Li
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Correspondence to Zhenhui Li .

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Editors and Affiliations

  1. IBM, Yorktown Heights, New York, USA

    Charu C. Aggarwal

  2. University of Illinois at Urbana-Champaign, Urbana, Illinois, USA

    Jiawei Han

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Li, Z. (2014). Spatiotemporal Pattern Mining: Algorithms and Applications. In: Aggarwal, C., Han, J. (eds) Frequent Pattern Mining. Springer, Cham. https://doi.org/10.1007/978-3-319-07821-2_12

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  • DOI: https://doi.org/10.1007/978-3-319-07821-2_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07820-5

  • Online ISBN: 978-3-319-07821-2

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