Novel Algorithm of Spatiotemporal Association Rules Mining Based on Event-coverage
Gang Fang, Yue Wu
Journal of Intelligent Computing, Vol. 7, No. 2, 2016.
DOI: 
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Abstract:
In order to eliminate data redundancy of spatiotemporal database, and flexibly create spatiotemporal association
patterns, and fast discover spatiotemporal association rules, firstly, this paper adopts event-coverage to create spatiotemporal
mining database; the method can divide the spatiotemporal domain into some spatiotemporal transaction cells, where
each cell is made of attribute values and spatiotemporal predicate values created by the concept generalization method. Then
we propose a novel algorithm of spatiotemporal association rules mining based on event-coverage, which can make each
spatiotemporal association pattern be mapped to a mixed radix numeral, and uses power set to compute the support. The
algorithm adopts simple data structure to discover frequent spatiotemporal association patterns, it only needs to read the
database once, and need not generate candidate for mining spatiotemporal association patterns. Because of them, the algorithm
overcomes these disadvantages of traditional classical algorithms for discovering frequent patterns. Finally, we discuss
the optimal application environments of the algorithm to mine spatiotemporal association rules. For discovering frequent
spatiotemporal association patterns on the application environments, these experimental results indicate that the algorithm
is better than these traditional classical mining frameworks, particularly, the Apriori framework and the FP-growth framework.