ACBC-Adequate Association and Decision Rules Versus Key Generators and Rough Sets Approximations

Marzena Kryszkiewicz

Abstract

In 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.
Author Marzena Kryszkiewicz II
Marzena Kryszkiewicz,,
- The Institute of Computer Science
Journal seriesFundamenta Informaticae, ISSN 0169-2968
Issue year2016
Vol148
No1-2
Pages65-85
Publication size in sheets1
DOIDOI:10.3233/FI-2016-1423
URL http://content.iospress.com/articles/fundamenta-informaticae/fi1423
projectDevelopment 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
WEiTI Działalność statutowa
Languageen angielski
Score (nominal)20
ScoreMinisterial score = 20.0, 27-03-2017, ArticleFromJournal
Ministerial score (2013-2016) = 20.0, 27-03-2017, ArticleFromJournal
Publication indicators WoS Impact Factor: 2016 = 0.687 (2) - 2016=0.775 (5)
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* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.
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