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These methods are, however, based on heuristics and greedy approaches to generate rule sets that are either too general or too overfitting for a given dataset.
The first method, (PP_MAR_MaxSC_1), includes three phases: (a) using dEclat to mine frequent itemsets, (b) integrating the constraints into the (Gen_Rules) [31] algorithm to generate rule candidates, and (c) post-processing to filter out the rules satisfying the constraints.
One method of generating niche models, the evolutionary computing algorithm GARP, uses a variety of rule-building methods in an iterative machine-learning process to generate rule sets [36].
The purpose of this step was also to generate rule sets with a balanced number of rules for the three conclusions.
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Thus, we generate rules within each common prefix sub-tree.
One is to generate rules by implementing PSO in the traditional algorithm of association rule mining.
Consequently, it is possible to generate rules directly without scanning all frequent sequences for prefix checking.
We used Apriori [22] algorithm in Weka 3.6.12 in order to generate rules.
Finally, an algorithm based on Apriori principle is applied to generate rules.
A new implementation of the cascade model was used to generate rules using the arules package.
The bounded-error approach is applied to generate rules for the model using available data.
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