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An Efficient Conceptual Clustering Scheme

효율적인 개념 클러스터링 기법

  • Yang, Gi-Chul (Department of Convergence Software, Mokpo National University)
  • 양기철 (목포대학교 융합소프트학과)
  • Received : 2020.05.15
  • Accepted : 2020.06.26
  • Published : 2020.06.30
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Abstract

This paper, firstly, propose a new Clustering scheme Based on Conceptual graphs (CBC) that can describe objects freely and can perform clustering efficiently. The conceptual clustering is one of machine learning technique. The similarity among the objects in conceptual clustering are decided on the bases of concept membership, unlike the general clustering scheme which decide the similarity without considering the context or environment of the objects. A new conceptual clustering scheme, CBC, which can perform efficient conceptual clustering by describing various objects freely with conceptual graphs is introduced in this paper.

본 논문에서는 개체를 자유롭게 설명하고 효율적으로 클러스터링을 수행 할 수 있는 개념 그래프 기반의 새로운 클러스터링 체계 Clustering scheme Based on Conceptual graphs(CBC)를 제안한다. 개념적 클러스터링은 기계 학습 기술 중 하나이다. 개념 클러스터링에서 개체 간의 유사성은 개체의 의미나 환경을 고려하지 않고 유사성을 결정하는 일반적인 클러스터링 체계와 달리 개념 구성원의 자격에 따라 결정된다. 이 논문에서는 다양한 개체를 개념 그래프로 자유롭게 설명하여 효율적인 개념 클러스터링을 수행 할 수 있는 새로운 개념 클러스터링 체계인 CBC를 소개한다.

Keywords

Acknowledgement

본 논문은 목포대학교의 2019학년도 학술연구 조성비를 지원받음.

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