Mining at most top-K% mixed-drove spatio-temporal co-occurrence patterns: A summary of results

Celik M. , Shekhar S., Rogers J. P. , Shine J. A. , Kang J. M.

IEEE 23rd International Conference on Data Engineering Workshop, İstanbul, Turkey, 17 - 20 April 2007, pp.565-566 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/icdew.2007.4401042
  • City: İstanbul
  • Country: Turkey
  • Page Numbers: pp.565-566


Mixed-drove spatio-temporal co-occurrence patterns (MDCOPs) represent subsets of object-types that are located together in space and time. Discovering MDCOPs is an important problem with many applications such as Planning battlefield tactics, and tracking predator-prey interactions. However, determining suitable interest measure thresholds is a difficult task In this paper, we define the problem of mining at most top-K% MDCOPs without using user defined thresholds and propose a novel at most top-K% MDCOP mining algorithm. Analytical and experimental results show that the proposed algorithm is correct and complete. Results show the proposed method is computationally more efficient than naive alternatives.