Title :
Spatio-temporal data mining with expected distribution domain generalization graphs
Author :
Hamilton, Howard J. ; Geng, Liqiang ; Findlater, Leah ; Randall, Dee Jay
Author_Institution :
Dept. of Comput. Sci., Regina Univ., Sask., Canada
Abstract :
We describe a method for spatio-temporal data mining based on expected distribution domain generalization (ExGen) graphs. Using familiar calendar and geographical concepts, such as workdays, weeks, climatic regions, and countries, spatio-temporal data can be aggregated into summaries in many ways. We automatically search for a summary with a distribution that is anomalous, i.e., far from user expectations. We repeatedly ranked possible summaries according to current expectations, and then allow the user to adjust these expectations.
Keywords :
data mining; temporal databases; visual databases; ExGen graphs; expected distribution domain generalization; spatio-temporal data mining; Calendars; Cities and towns; Computer science; Cultural differences; Data analysis; Data mining; Data visualization; Earth; Information analysis; Sun;
Conference_Titel :
Temporal Representation and Reasoning, 2003 and Fourth International Conference on Temporal Logic. Proceedings. 10th International Symposium on
Print_ISBN :
0-7695-1912-1
DOI :
10.1109/TIME.2003.1214895