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, Türkiye, 17 - 20 Nisan 2007, ss.565-566 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/icdew.2007.4401042
  • Basıldığı Şehir: İstanbul
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.565-566
  • Erciyes Üniversitesi Adresli: Evet

Özet

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.