Spatio-temporal data mining with expected distribution domain generalization graphs

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. W...

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Main Authors: Hamilton, H.J., Liqiang Geng, Findlater, L., Randall, D.J.
Format: Conference Proceeding
Language:English
Subjects:
Calendars
Cities and towns
Computer science
Cultural differences
Data analysis
Data mining
Data visualization
Earth
Information analysis
Sun
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creator Hamilton, H.J.
Liqiang Geng
Findlater, L.
Randall, D.J.
description 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.
doi_str_mv 10.1109/TIME.2003.1214895
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source IEEE Electronic Library (IEL) Conference Proceedings
subjects Calendars
Cities and towns
Computer science
Cultural differences
Data analysis
Data mining
Data visualization
Earth
Information analysis
Sun
title Spatio-temporal data mining with expected distribution domain generalization graphs
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