Due to the advances in remote sensors and sensor networks, spatial and spatio-temporal data become increasingly available. In this talk, we present two different approaches to find interesting regions is such datasets; one approach is continuous function-based, whereas the second approach transforms datasets into graph and then mines those graphs. The first approach is a serial, density-based spatial-temporal clustering approach that employs non-parametric density estimation techniques and contouring algorithms to obtain spatial clusters whose scope is described using polygon models; next, it identifies spatio-temporal clusters as continuing polygons in consecutive time frames. The second approach is graph-based interestingness hotspot discovery framework—it employs Gabriel graphs to define object neighborhoods—which grows hotspots from hotspot seeds, maximizing a plug-in interestingness function. Finally, experimental results obtained by applying the two approaches to flood risk mapping, air pollution, earthquake and New York taxi cab datasets are discussed.
Christoph F. Eick is an Associate Professor in the Department of Computer Science at the University of Houston and the Director of the UH Data Analysis and Intelligent Systems Lab. His research interests include data sciences, data mining, geographical information systems, artificial intelligence and disaster computing. He published more than 160 papers in these areas. He also serves in the program committee of top data mining and artificial intelligence conferences.