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Visual Analytical Tool for Higher Order k-Means Clustering for Trajectory Data Mining

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AI 2016: Advances in Artificial Intelligence (AI 2016)

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Abstract

Trajectories are useful sources to understand moving objects and locations. Many trajectory data mining techniques have been researched in the past decade. Higher order information providing suggestions to what-if analysis when the best possible option is not feasible is of importance in dynamic and complex spatial environments. Despite of the importance of higher order information in trajectory data mining, it has received little attention in literature. This paper introduces new visualisation methods for determination of higher order k-means clustering for trajectory data mining. This paper proposes a radar chart-like visualisation for geometrical and directional higher order information and a k-means clustering technique for trajectory higher order information. This paper also demonstrates the usefulness of proposed visualisation methods and clustering technique with a case study using real world datasets.

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Authors and Affiliations

  1. College of Business, Law and Governance, Information Technology Academy, James Cook University, Cairns, QLD, 4870, Australia

    Ye Wang, Kyungmi Lee & Ickjai Lee

Authors
  1. Ye Wang
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  2. Kyungmi Lee
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  3. Ickjai Lee
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Corresponding author

Correspondence to Ickjai Lee .

Editor information

Editors and Affiliations

  1. University of Tasmania, Hobart, Australia

    Byeong Ho Kang

  2. Auckland University of Technology, Auckland, New Zealand

    Quan Bai

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Wang, Y., Lee, K., Lee, I. (2016). Visual Analytical Tool for Higher Order k-Means Clustering for Trajectory Data Mining. In: Kang, B.H., Bai, Q. (eds) AI 2016: Advances in Artificial Intelligence. AI 2016. Lecture Notes in Computer Science(), vol 9992. Springer, Cham. https://doi.org/10.1007/978-3-319-50127-7_43

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  • DOI: https://doi.org/10.1007/978-3-319-50127-7_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50126-0

  • Online ISBN: 978-3-319-50127-7

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