Central Concept and Its Application in Rule Extraction

Abstract

Rule extraction of decision information system is one of the important research contents of data analysis. Formal concept analysis is efficient for data analysis and information processing. Based on formal concept, com-prehensive concepts and central concepts are defined in this paper, and then the algorithm for concise rule extraction is proposed. In this process, the decision attributes of the formal decision context take participate in the generation of the concepts. After removing redundant comprehensive concepts at each level, the rest parts are central concepts and they are the decision rules. The central concepts reflect the implication relationship between conditional attributes and decision attributes in information systems, and make formal concepts with richer knowledge. Meanwhile they also effectively combine decision rules with concepts together. The corresponding Hasse diagram is more meaningful than the traditional one. The proposed algorithm avoids the complexity of traditional methods which have to generate conditional concepts and decision concepts respectively and then to get rules by complex computation. Finally, the effectiveness and correctness of the algorithm are verified by case analysis and comparative experiments with UCI datasets.

Keywords

• decision information system
• comprehensive concept
• central concept
• rule extraction