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Trajectory Clustering Analysis

In: Machine Learning for Data Science Handbook

Author

Listed:
  • Yulong Wang

    (Huazhong Agricultural University)

  • Yuan Yan Tang

    (Avenida da Universidade, University of Macau)

Abstract

In this chapter, we will introduce the development of trajectory clustering analysis. First, we review some related works on clustering of trajectory data, especially including the subspace clustering-based methods. Second, we depict a general framework, termed as atomic-representation-based subspace clustering (ARSC) for the clustering of trajectory data. ARSC is a subspace clustering framework by first computing the atomic representations of data points and then clustering them using the representations. By using ARSC as a general platform, we introduce a robust subspace clustering method that is referred as minimum error entropy-based sparse subspace clustering (MEESSC) against outliers and heavy data noises. MEESSC computes the representation of each data point by minimizing the ℓ1 norm regularized minimum error entropy-based loss function. Experimental results are shown to validate the efficacy and robustness of MEESSC for the clustering of trajectory data.

Suggested Citation

  • Yulong Wang & Yuan Yan Tang, 2023. "Trajectory Clustering Analysis," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 197-217, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-24628-9_10
    DOI: 10.1007/978-3-031-24628-9_10
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