TY - GEN
T1 - Mixed-drove spatio-temporal co-occurrence pattern mining
T2 - 6th International Conference on Data Mining, ICDM 2006
AU - Celik, Mete
AU - Shekhar, Shashi
AU - Rogers, James P.
AU - Shine, James A.
AU - Yoo, Jin Soung
PY - 2006
Y1 - 2006
N2 - 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 identifying tactics in battlefields, games, and predator-prey interactions. However, mining MDCOPs 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 monotonie composite interest measure for discovering MDCOPs and a novel MDCOP mining algorithm. Analytical and experimental results show that the proposed algorithm is correct and complete. Results also show the proposed method is computationally more efficient than naïve alternatives.
AB - 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 identifying tactics in battlefields, games, and predator-prey interactions. However, mining MDCOPs 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 monotonie composite interest measure for discovering MDCOPs and a novel MDCOP mining algorithm. Analytical and experimental results show that the proposed algorithm is correct and complete. Results also show the proposed method is computationally more efficient than naïve alternatives.
UR - http://www.scopus.com/inward/record.url?scp=84878043307&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84878043307&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2006.112
DO - 10.1109/ICDM.2006.112
M3 - Conference contribution
AN - SCOPUS:84878043307
SN - 0769527019
SN - 9780769527017
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 119
EP - 128
BT - Proceedings - Sixth International Conference on Data Mining, ICDM 2006
Y2 - 18 December 2006 through 22 December 2006
ER -