Rough set‐based rule generation and Apriori‐based rule generation from table data sets II: SQL‐based environment for rule generation and decision support. Issue 4 (12th September 2019)

• Record Type: Journal Article
• Authors: Sakai, Hiroshi; Jian, Zhiwen
• Is Part Of: CAAI transactions on intelligence technology. Volume 4:Issue 4(2019)
• Page Start: 214
• Page End: 222
• Publication Date: 2019-09-12
• Subjects: rough set theory -- SQL -- data mining -- decision support systems; Apriori‐based rule generation -- SQL‐based environment -- decision support -- table data sets -- rule generator -- rough set‐based rule generation; C1160 Combinatorial mathematics -- C6170K Knowledge engineering techniques -- C7102 Decision support systems; Artificial intelligence -- Periodicals; Computer science -- Periodicals; Artificial intelligence; Computer science; Electronic journals; Periodicals; 006.305
• DOI: 10.1049/trit.2019.0016
• Languages: English
• ISSNs: 2468-6557

Abstract

Abstract : 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.