Efficient Mining of Emerging Events in a Dynamic Spatiotemporal Environment

  • Yu Meng
  • Margaret H. Dunham
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3918)


This paper presents an efficient data mining technique for modeling multidimensional time variant data series and its suitability for mining emerging events in a spatiotemporal environment. The data is modeled using a data structure that interleaves a clustering method with a dynamic Markov chain. Novel operations are used for deleting obsolete states, and finding emerging events based on a scoring scheme. The model is incremental, scalable, adaptive, and suitable for online processing. Algorithm analysis and experiments demonstrate the efficiency and effectiveness of the proposed technique.


Markov Chain Model Concept Drift Cluster Feature Efficient Mining Time Series Database 
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

  • Yu Meng
    • 1
  • Margaret H. Dunham
    • 1
  1. 1.Department of Computer Science and EngineeringSouthern Methodist UniversityDallasUSA

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