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Role-Behavior Analysis from Trajectory Data by Cross-Domain Learning

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2 Author(s)
Shin Ando ; Dept. of Comput. Sci., Gunma Univ., Gunma, Japan ; Einoshin Suzuki

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.

Published in:

2011 IEEE 11th International Conference on Data Mining

Date of Conference:

11-14 Dec. 2011