Sustained emerging spatio-temporal co-occurrence pattern mining: A summary of results

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

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

25 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProcedings - 18th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2006
Pages106-115
Number of pages10
DOIs
StatePublished - Dec 1 2006
Event18th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2006 - Arlington, VA, United States
Duration: Oct 13 2006Oct 15 2006

Publication series

NameProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
ISSN (Print)1082-3409

Other

Other18th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2006
Country/TerritoryUnited States
CityArlington, VA
Period10/13/0610/15/06

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