Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Generation of Decision Rules Bsed on Concept Ascension and Optimal Reduction of Attributes

개념 상승과 속성의 최적 감축에 의한 결정 규칙의 생성

  • 정환묵 (대구효성가톨릭대학교 전자정보공학과)
  • Published : 1999.08.01
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Abstract

This paper suggests an integrated method based on concept ascension and attribute reduction for efficient induction of decision rules from a large database. We study an automatic scheme to generate concept trees by a clustering technique, a method for generalizing databases by the concept ascension technique, an optimal reduction method by means of attributes reduction using the sibmificance of attributes, and an efficient way of reduction of attribute values applying the discernible matrix and functions. The method can be used for the decision making tasks such as an investment planning or price evaluation, the construction of knowledge bases for diagnosis of defects or medical diagnosis, data analysis such as marketing or experimental data, information retrieval for high level inquiries, and so on.

본 논문은 대규모 데이터베이스에서 의사 결정을 위한 지식을 효율적으로 추출하기 위해 개념 상승과 속성 감축에 기반한 통합적 방법을 제안한다. 본 방법은 클리스터링 기법에 의해 개념 트리를 자동생성하고 개념 상승기법에 의해 데이터 베이스를 일반화하며 속성의 중요도를 사용한 속성 감축에 의해 최적감축을 하고 식별가능 행렬과 함수를 사용하여 효율적으로 속성값을 감축하여 최적의 최소결정 규칙을 유도한다. 본 방법은 투자 계획이나 가격 결정과 같은 의사결정 업무 각종 고장 진단이나 의료 진단을 위한 지식 베이스구축 마케팅 분석이나 실험 데이터 분석 고수준의 질의 에 의한 정보검색 등에 효과적으로 사용될수 있다.

Keywords

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