Sustained Emerging Spatio-Temporal Co-occurrence Pattern Mining: A Summary of Results

Authors:
Mete Celik;Shashi Shekhar;James P. Rogers;James A. Shine
Affiliations:
University of Minnesota, USA;University of Minnesota, USA;U.S. Army ERDC, Topographic Engineering Center, USA;U.S. Army ERDC, Topographic Engineering Center, USA
Venue:
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Year:
2006

Citing 0
Cited 3

Composite Spatio-Temporal Co-occurrence Pattern Mining

WASA '08 Proceedings of the Third International Conference on Wireless Algorithms, Systems, and Applications
Mining Spread Patterns of Spatio-temporal Co-occurrences over Zones

ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part II
Cascading an emerging pattern based classifier

MCPR'10 Proceedings of the 2nd Mexican conference on Pattern recognition: Advances in pattern recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

Sustained emerging spatio-temporal co-occurrence patterns (SECOPs) represent subsets of object-types that are increasingly located together in space and time. Discovering SECOPs is important due to many applications, e.g., predicting emerging infectious diseases, predicting defensive and offensive intent from troop movement patterns, and novel predator-prey interactions. However, mining SECOPs is computationally very expensive because the interest measures are computationally complex, datasets are larger due to the archival history, and the set of candidate patterns is exponential in the number of object-types. We propose a monotonic interest measure for mining SECOPs and a novel SECOP mining algorithm. Analytical and experimental results show that the proposed algorithm is correct, complete, and computationally faster than related approaches. Results also show the proposed algorithm is computationally more efficient than naïve alternatives.