Granules for association rules and decision support in the getRNIA system

Issue title: Granular Knowledge Discovery

Guest editors: Dominik Ślȩzak, Andrzej Skowron, Yukio Ohsawa and Junzo Watada

Article type: Research Article

Authors: Sakai, Hiroshi^{a; *} | Wu, Mao^a | Yamaguchi, Naoto^a | Nakata, Michinori^b

Affiliations: [a] Graduate School of Engineering, Kyushu Institute of Technology, Tobata, Kitakyushu, Japan | [b] Faculty of Management and Information Science, Josai International University, Togane, Chiba, Japan

Correspondence: [*] Corresponding author: Hiroshi Sakai, Graduate School of Engineering, Kyushu Institute of Technology, Tobata, Kitakyushu 804-8550, Japan. Tel.: +81 93 884 3258; E-mail:[email protected]

Abstract: This paper proposes granules for association rules in Deterministic Information Systems (DISs) and Non-deterministic Information Systems (NISs). Granules for an association rule are defined for every implication, and give us a new methodology for knowledge discovery and decision support. We see that decision support based on a table under the condition P is to fix the decision Q by using the most proper association rule P\Rightarrow Q. We recently implemented a system getRNIA powered by granules for association rules. This paper describes how the getRNIA system deals with decision support under uncertainty, and shows some results of the experiment.

Keywords: Granules, rough sets, association rules, software, incomplete information, non-deterministic information

DOI: 10.3233/IDT-140226

Journal: Intelligent Decision Technologies, vol. 9, no. 4, pp. 309-320, 2015