TrajPattern: Mining Sequential Patterns from Imprecise Trajectories of Mobile Objects

  • Jiong Yang
  • Meng Hu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3896)


Mobile objects have become ubiquitous in our everyday lives, ranging from cellular phones to sensors, therefore, analyzing and mining mobile data becomes an interesting problem with great practical importance. For instance, by finding trajectory patterns of the mobile clients, the mobile communication network can allocate resources more efficiently. However, due to the limited power of the mobile devices, we are only able to obtain the imprecise location of a mobile object at a given time. Sequential patterns are a popular data mining model. By applying the sequential pattern model on the set of imprecise trajectories of the mobile objects, we may uncover important information or further our understanding of the inherent characteristics of the mobile objects, e.g., constructing a classifier based on the discovered patterns or using the patterns to improve the accuracy of location prediction. Since the input data is highly imprecise, it may not be possible to directly apply any existing sequential pattern discovery algorithm to the problem in this paper. Thus, we propose the model of the trajectory patterns and a novel measure to represent the expected occurrences of a pattern in a set of imprecise trajectories. The concept of pattern groups is introduced to present the trajectory patterns in a concise manner. Since the Apriori property no longer holds on the trajectory patterns, a new min-max property is identified and a novel TrajPattern algorithm is devised based on the newly discovered property. Last but not least, we apply the TrajPattern algorithm on a wide range of real and synthetic data sets to demonstrate the usefulness, efficiency, and scalability of this approach.


Sequential Pattern Location Prediction Mobile Object Pattern Group Projection Base 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jiong Yang
    • 1
  • Meng Hu
    • 1
  1. 1.EECSCase Western Reserve University 

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