A Robust Approach to Subsequence Matching

  • Aihua Zheng
  • Jixin Ma
  • Miltos Petridis
  • Jin Tang
  • Bin Luo
Part of the Studies in Computational Intelligence book series (SCI, volume 253)

Summary

In terms of a general time theory which addresses time-elements as typed point-based intervals, a formal characterization of time-series and state-sequences is introduced. Based on this framework, the subsequence matching problem is specially tackled by means of being transferred into bipartite graph matching problem. Then a hybrid similarity model with high tolerance of inversion, crossover and noise is proposed for matching the corresponding bipartite graphs involving both temporal and non-temporal measurements. Experimental results on reconstructed time-series data from UCI KDD Archive demonstrate that such an approach is more effective comparing with the traditional similarity model based algorithms, promising robust techniques for lager time-series databases and real-life applications such as Content-based Video Retrieval (CBVR), etc.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Aihua Zheng
    • 1
    • 2
  • Jixin Ma
    • 2
  • Miltos Petridis
    • 2
  • Jin Tang
    • 1
  • Bin Luo
    • 1
  1. 1.Anhui UniversityHefeiPeople’s Republic of China
  2. 2.The University of Greenwich, GreenwichLondonUnited Kingdom

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