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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationWorkshops in Conjunction with the International Conference on Data Engineering - ICDE' 07
Pages565-574
Number of pages10
DOIs
StatePublished - Dec 1 2007
EventWorkshops in Conjunction with the 23rd International Conference on Data Engineering - ICDE 2007 - Istanbul, Turkey
Duration: Apr 15 2007Apr 20 2007

Other

OtherWorkshops in Conjunction with the 23rd International Conference on Data Engineering - ICDE 2007
Country/TerritoryTurkey
CityIstanbul
Period4/15/074/20/07

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