Mining At Most Top-K% Mixed-drove Spatio-temporal Co-occurrence Patterns: A Summary of Results

  • Authors:

  • Mete Celik;Shashi Shekhar;James P. Rogers;James A. Shine;James M. Kang

  • Affiliations:

  • Department of Computer Science, University of Minnesota, MN, USA. mcelik@cs.umn.edu;Department of Computer Science, University of Minnesota, MN, USA. shekhar@cs.umn.edu;U.S. Army ERDC, Topographic Engineering Center, VA, USA. james.p.rogers.II@erdc.usace.army.mil;U.S. Army ERDC, Topographic Engineering Center, VA, USA. james.a.shine@erdc.usace.army.mil;Department of Computer Science, University of Minnesota, MN, USA. jkang@cs.umn.edu

  • Venue:
  • ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
  • Year:
  • 2007

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Abstract

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 naïve alternatives.