TraClass: Trajectory Classification Using Hierarchical Region-Based and Trajectory-Based Clustering

Trajectory classi¯cation, i.e., model construction for predict- ing the class labels of moving objects based on their trajecto- ries and other features, has many important, real-world ap- plications. A number of methods have been reported in the literature, but due to using the shapes of whole trajectories for classi¯cation, they have limited classi¯cation capability when discriminative features appear at parts of trajectories or are not relevant to the shapes of trajectories. These situ- ations are often observed in long trajectories spreading over large geographic areas. Since an essential task for e®ective classi¯cation is gen- erating discriminative features, a feature generation frame- work TraClass for trajectory data is proposed in this pa- per, which generates a hierarchy of features by partition- ing trajectories and exploring two types of clustering: (1) region-based and (2) trajectory-based. The former captures the higher-level region-based features without using move- ment patterns, whereas the latter captures the lower-level trajectory-based features using movement patterns. The proposed framework overcomes the limitations of the previ- ous studies because trajectory partitioning makes discrimi- native parts of trajectories identi¯able, and the two types of clustering collaborate to ¯nd features of both regions and sub-trajectories. Experimental results demonstrate that TraClass generates high-quality features and achieves high classi¯cation accuracy from real trajectory data.
Date: August 02, 2008
Book Title: Int. Conf. on Very Large Data Base (VLDB'08)
Type: InProceedings
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@InProceedings{TraClass_Trajectory_Classification_Using,
  author = "Jae-Gil Lee and Jiawei Han and Xiaolei Li and Hector Gonzalez",
  title = "{TraClass: Trajectory Classification Using Hierarchical Region-Based and Trajectory-Based Clustering}",
  month = "August",
  year = "2008",
  booktitle = "Int. Conf. on Very Large Data Base (VLDB'08)",
}