Open AccessRough set‐based rule generation and Apriori‐based rule generation from table data sets II: SQL‐based environment for rule generation and decision support
Author(s) -
Sakai Hiroshi,
Jian Zhiwen
Publication year - 2019
Publication title -
caai transactions on intelligence technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.613
H-Index - 15
ISSN - 2468-2322
DOI - 10.1049/trit.2019.0016
Subject(s) - data mining , decision table , sql , rough set , decision rule , computer science , generator (circuit theory) , apriori algorithm , rule based system , association rule learning , table (database) , decision support system , set (abstract data type) , rule induction , decision tree , database , artificial intelligence , programming language , power (physics) , physics , quantum mechanics
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.