Authors:
Chun-Che Huang
;
Shian-Hua Lin
;
Zhi-Xing Chen
and
You-Ping Wang
Affiliation:
National Chi Nan University, Taiwan
Keyword(s):
Concept Hierarchy, Rough Set Theory, Rule Induction, Decision Making.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Problem Solving
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
Hierarchical attributes are usually predefined in real-world applications and can be represented by a concept hierarchy, which is a kind of concise and general form of concept description that organizes relationships of data. To induct rules from the qualitative and hierarchical nature in data, the rough set approach is one of the promised solutions in data mining. However, previous rough set approaches induct decision rules that contain the decision attribute in the same hierarchical level. In addition, comparison of the reducts using the Strength Index (SI), which is introduced to identify meaningful reducts, is limited to same number of attributes. In this paper, a hierarchical rough set (HRS) problem is defined and the solution approach is proposed. The proposed solution approach is expected to increase potential benefits in decision making.