Mete Celik, Student Member, Shashi Shekhar, James P. Rogers, James A. Shine
Abstract—Mixed-drove spatio-temporal co-occurrence patterns (MDCOPs) represent subsets of two or more different object-types whose instances are often located in spatial and temporal proximity....
Mete Celik, Shashi Shekhar, James P. Rogers, James A. Shine, Jin Soung Yoo
Detecting IEDs, suspicious vehicles Detecting patterns of enemy troop movement (manpack stinger, tank, and truck)
Spatial Dependency Modeling Using Spatial Auto-Regression * (2008)
Mete Celik, Baris M. Kazar, Shashi Shekhar, Daniel Boley, David J. Lilja
Parameter estimation of the spatial auto-regression model (SAR) is important because we can model the spatial dependency, i.e., spatial autocorrelation present in the geo-spatial data. SAR is a...
Parallelizing Multiscale and Multigranular Spatial Data Mining Algorithms (2008)
Vijay G, Mete Celik, Shashi Shekhar
Multiscale and Multigranular (MSMG) Spatial Data Mining (SDM) algorithms are used to find the best granular class label from a hierarchical set of granular class labels for spatial classification,...
Zonal Co-location Pattern Discovery with Dynamic Parameters (2008)
Mete Celik, James M. Kang, Shashi Shekhar
Zonal co-location patterns represent subsets of featuretypes that are frequently located in a subset of space (i.e., zone). Discovering zonal spatial co-location patterns is an important problem with...
Mining at most top-k mixed-drove spatio-temporal co-occurrence patterns: A summary of results (2007)
Mete Celik, Shashi Shekhar, James P. Rogers, James A. Shine, James M. Kang
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...
Mixed-drove spatio-temporal co-occurrence pattern mining: A summary of results (2006)
Mete Celik, Shashi Shekhar, James P. Rogers, James A. Shine, Jin Soung Yoo
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...
Mixed-drove spatio-temporal co-occurrence pattern mining: A summary of results (2006)
Mete Celik, Shashi Shekhar, James P. Rogers, James A. Shine, Jin Soung Yoo
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...
A join-less approach for co-location pattern mining: A summary of results (2005)
Jin Soung Yoo, Shashi Shekhar, Mete Celik
Spatial co-location patterns represent the subsets of features whose instances are frequently located together in geographic space. Co-location pattern discovery presents challenges since the...