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To select the interesting rules, minimum threshold value of support is provided.
Eventually, the interesting rules according to the decision makers' preferences are presented in Table 10 from Order 1 to Order 16.
This approach consists of two major steps; a rules generator using the Apriori algorithm to extract association rules, and multi-criteria decision analysis to evaluate and select the interesting rules from the large set extracted.
On the other side, our proposed approach of MCA allows the decision makers to select the interesting rules according to their prespecific needs; in addition, the integration of the MCA approach allows users to solve the complex situation by selecting only the significant and useful rules.
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Indeed, the final stage of rule validation will let the user face the main difficulty: like the selection of the most interesting rules among the large number of extracted rules.
The group of the most interesting rules according to the lift (this measures how far the antecedent and consequent rules are from independence) are shown in the top left-hand corner of the plot.
Furthermore, the integration of MCDA within the association rule mining process provides a sustainable solution by selecting only the most interesting rules according to the decision makers' preferences.
However, these algorithms produce a large number of rules, which do not allow the decision makers to make their own choice of the most interesting rules.
In data mining field, the extraction algorithms produce a large number of association rules that not allow the decision makers to make their own choice of the most interesting rules.
The results of our study not only confirm an association between certain variables but also show that the integration of MCDA allows decision makers to make their own selection of the most interesting rules, according to their preferences and needs, allowing the application of accident prevention efforts in the identified areas for various categories of accidents.
The most interesting rules are given in Table 9.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com