Mining Association Rules in Spatio-Temporal Data
نویسندگان
چکیده
This research demonstrates the application of association rule mining to spatiotemporal data. Association rule mining seeks to discover associations among transactions encoded in a database. An association rule takes the form A ? B where A (the antecedent) and B (the consequent) are sets of predicates. A spatiotemporal association rule occurs when there is a spatio-temporal relationship in the antecedent or consequent of the rule. As a case study, association rule mining is used to explore the spatial and temporal relationships among a set of variables that characterize socioeconomic and land cover change in the Denver, Colorado, U.S.A. region from 1970 – 1990. Geographic Information Systems (GIS)-based data pre-processing is used to integrate diverse data sets, extract spatio-temporal relationships, classify numeric data into ordinal categories, and encode spatiotemporal relationship data in tabular format for use by conventional (non-spatiotemporal) association rule mining software. Multiple level association rule mining is supported by the development of a hierarchical classification scheme (concept hierarchy) for each variable. Further research in spatio-temporal association rule mining should address issues of data integration, data classification, the representation and calculation of spatial relationships, and strategies for finding ‘interesting’ rules.
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Events-coverage based spatio-temporal association rules mining method
Spatio-temporal association rules mining is a key technology and a hot issue in the field of spatio-temporal data mining. The classical Apriori algorithm is usually utilized to detect the spatio-temporal association rules from the spatio-temporal transaction table, which is derived from the original spatio-temporal data. In most existing approaches to generate the spatio-temporal transaction ta...
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