Composite Spatio-Temporal Co-occurrence Pattern Mining

  • Zhongnan Zhang
  • Weili Wu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5258)


Spatio-temporal co-occurrence patterns (STCOPs) represent subsets of features that are located together in space and time. Mining such patterns is important for many spatio-temporal application domains. However, a co-occurrence analysis across multiple spatio-temporal datasets is computationally expensive when the dimension of the time series and number of locations in the spaces are large. In this paper, we first defined STCOPs and the STCOPs mining problem. We proposed a monotonic composite measure, which is the composition of the spatial prevalence and temporal prevalence measures. A novel and computationally efficient algorithm, Costcop  + , is presented by applying the composite measure. We proved that the proposed algorithm is correct and complete in finding STCOPs. Using a real dataset, the experiments illustrate that the algorithm is efficient.


Time Slot Mining Algorithm Spatial Framework Candidate Pattern Participation Ratio 
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 2008

Authors and Affiliations

  • Zhongnan Zhang
    • 1
  • Weili Wu
    • 1
  1. 1.Department of Computer ScienceThe University of Texas at DallasRichardsonUSA

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