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

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, determi...

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Bibliographic Details
Published in2007 IEEE 23rd International Conference on Data Engineering Workshop pp. 565 - 574
Main Authors Celik, M., Shekhar, S., Rogers, J.P., Shine, J.A., Kang, J.M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2007
Subjects
Algae
Algorithm design and analysis
Animals
Application software
Computer science
Contracts
Environmental factors
Military computing
Sea measurements
Strategic planning
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Summary: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.
ISBN:9781424408313
1424408318
DOI:10.1109/ICDEW.2007.4401042