Trajectory Clustering Analysis

Yulong Wang () and Yuan Yan Tang ()
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Yulong Wang: Huazhong Agricultural University
Yuan Yan Tang: Avenida da Universidade, University of Macau

A chapter in Machine Learning for Data Science Handbook, 2023, pp 197-217 from Springer

Abstract: 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.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-24628-9_10

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DOI: 10.1007/978-3-031-24628-9_10

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