Role-behavior analysis from trajectory data by cross-domain learning

Shin Ando, Einoshin Suzuki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Behavior analysis using trajectory data presents a practical and interesting challenge for KDD. Conventional analyses address discriminative tasks of behaviors, e.g., classification and clustering typically using the subsequences extracted from the trajectory of an object as a numerical feature representation. In this paper, we explore further to identify the difference in the high-level semantics of behaviors such as roles and address the task in a cross-domain learning approach. The trajectory, from which the features are sampled, is intuitively viewed as a domain, and we assume that its intrinsic structure is characterized by the underlying role associated with the tracked object. We propose a novel hybrid method of spectral clustering and density approximation for comparing clustering structures of two independently sampled trajectory data and identifying patterns of behaviors unique to a role. We present empirical evaluations of the proposed method in two practical settings using real-world robotic trajectories.

Original languageEnglish
Title of host publicationProceedings - 11th IEEE International Conference on Data Mining, ICDM 2011
Pages21-30
Number of pages10
DOIs
Publication statusPublished - Dec 1 2011
Event11th IEEE International Conference on Data Mining, ICDM 2011 - Vancouver, BC, Canada
Duration: Dec 11 2011Dec 14 2011

Other

Other11th IEEE International Conference on Data Mining, ICDM 2011
CountryCanada
CityVancouver, BC
Period12/11/1112/14/11

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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    Ando, S., & Suzuki, E. (2011). Role-behavior analysis from trajectory data by cross-domain learning. In Proceedings - 11th IEEE International Conference on Data Mining, ICDM 2011 (pp. 21-30). [6137206] https://doi.org/10.1109/ICDM.2011.125