Abstract
AbstractIn spatial data mining, co-location pattern mining is intended to discover the sets of spatial features whose instances occur frequently in nearby geographic areas. Co-location pattern mining is an important task in spatial data mining and has been applied in many applications. Although many spatial co-location pattern mining methods applied to point objects have been proposed, few mining approaches are designed for extended spatial objects in practical scenes. Unlike existing works, we develop OESCPM (Online Extended Spatial Co-location Pattern Mining System) to mine co-location patterns over extended objects. OESCPM decomposes extended instances into cells and counts the number of cells belonging to different features. With simple interaction on OESCPM, users can view co-location patterns over interested extended spatial datasets online.KeywordsSpatial data miningCo-location patternExtended spatial objectsOnline mining
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