Optimal rule discovery uncovers rules that maximize an interestingness measure. The search for maxima further prunes the search space and, hence, optimal rule ...
We theoretically and empirically show that optimal rule discovery is significantly more efficient than association rule discovery independent of data structure ...
Here, we present a unified framework for the discovery of a family of optimal rule sets and characterize the relationships with other rule-discovery schemes ...
This work presents a unified framework for the discovery of a family of optimal rule sets and characterize the relationships with other rule-discovery ...
We theoretically and empirically show that optimal rule discovery is significantly more efficient than association rule discovery independent of data structure ...
Association bundles are a new pattern for association analysis that were presented by Huang et al. in 2006 as an alternative to association rules.
The main objective of this study was to offer the evidence against the above belief. We aimed to find typical patterns among stroke occurrences in case the ...
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Here we present a unified framework for the discovery of a family of optimal rule sets, and characterise the relationships with other rule discovery schemes ...
K -optimal rule discovery finds the K rules that optimize a user-specified measure of rule value with respect to a set of sample data and user-specified ...
Summary. The Neyman–Pearson lemma provides a simple procedure for optimally testing a single hypothesis when the null and alternative distributions are ...