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access icon openaccess Rough set-based rule generation and Apriori-based rule generation from table data sets II: SQL-based environment for rule generation and decision support

This is an open access article published by the IET, Chinese Association for Artificial Intelligence and Chongqing University of Technology under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 15/03/2019, Accepted 08/06/2019, Revised 21/05/2019, Published 01/07/2019

This study follows the previous study entitled ‘Rough set-based rule generation and Apriori-based rule generation from table data sets: A survey and a combination’, and this is the second study on ‘Rough set-based rule generation and Apriori-based rule generation from table data sets’. The theoretical aspects are described in the previous study, and here the aspects of application, an SQL-based environment for rule generation and decision support, are described. At first, the implementation of rule generator defined in the previous study is explained, then the application of the obtained rules to decision support is considered. Especially, the following two issues are focused on, (i) Rule generator from table data sets with uncertainty in SQL, (ii) The manipulation in decision support below: (ii-a) In the case that an obtained rule matches the condition, (ii-b) In the case that any obtained rule does not match the condition. The authors connect such cases with decision support and realised an effective decision support environment in SQL.

Inspec keywords: decision support systems; SQL; rough set theory; data mining

Other keywords: decision support; rule generator; Apriori-based rule generation; table data sets; SQL-based environment; rough set-based rule generation

Subjects: Combinatorial mathematics; Knowledge engineering techniques; Decision support systems

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