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Abstract
This paper presents a temporal association rule model for evolving numerical
attributes. Metrics for qualifying a temporal association rule include the
familiar measures of support and strength used in the traditional association
rule mining and a new metric called density. The density metric not only gives
us a way to extract the rules that best represent the data, but also provides an
effective mechanism to prune the search space. An efficient algorithm is devised
for mining temporal association rules, which utilizes all three thresholds to
prune the search space drastically. Moreover, the resulting rules are
represented in a concise manner via rule sets to reduce the output size.
Experimental results on real and synthetic data sets demonstrate the efficiency
of the proposed algorithm.
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