ACBC-Adequate Association and Decision Rules Versus Key Generators and Rough Sets Approximations
AbstractIn this paper, we propose an ACBC-evaluation formula, which delivers a flexible way of formulating different kinds of criteria for association and decision rules. We prove that rules with minimal antecedents that fulfill ACBC-evaluation formulae are key generators, which are patterns of a special type. We also show that a number of types of rough set approximations of decision classes can be expressed based on ACBC-evaluation formulae. We prove that decision rules preserving respective approximations of decision classes are rules that satisfy an ACBC-evaluation formula and that antecedents of such optimal decision rules are key generators, too. A number of properties related to particular measures of association rules and key generators are derived.
|Journal series||Fundamenta Informaticae, ISSN 0169-2968|
|Publication size in sheets||1|
|ASJC Classification||; ; ;|
|Project||Development of new algorithms in the areas of software and computer architecture, artificial intelligence and information systems and computer graphics . Project leader: Arabas Jarosław,
, Phone: +48 22 234 7432, start date 15-04-2016, end date 30-11-2017, II/2016/DS/1, Completed
|Score|| = 20.0, 02-02-2020, ArticleFromJournal|
= 20.0, 02-02-2020, ArticleFromJournal
|Publication indicators||= 1; = 1; = 1.0; : 2016 = 0.712; : 2016 = 0.687 (2) - 2016=0.775 (5)|
|Citation count*||1 (2020-09-06)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.