The Journal of Korean Institute of Communications and Information Sciences (한국통신학회논문지)

The Korean Institute of Commucations and Information Sciences (한국통신학회)
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Modeling of Self-Constructed Clustering and Performance Evaluation

자기-구성 클러스터링의 모델링 및 성능평가

  • 유정웅 (충북대학교 전기전자컴퓨터공학부) ;
  • 김승석 (충북대학교 전기전자컴퓨터공학부) ;
  • 송창규 (충북대학교 전기전자컴퓨터공학부) ;
  • 김성수 (충북대학교 전기전자컴퓨터공학부)
  • Published : 2005.06.01
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Abstract

In this paper, we propose a self-constructed clustering algorithm based on inference information of the fuzzy model. This method makes it possible to automatically detect and optimize the number of cluster and parameters by using input-output data. The propose method improves the performance of clustering by extended supervised learning technique. This technique uses the output information as well as input characteristics. For effect the similarity measure in clustering, we use the TSK fuzzy model to sent the information of output. In the conceptually, we design a learning method that use to feedback the information of output to the clustering since proposed algorithm perform to separate each classes in input data space. We show effectiveness of proposed method using simulation than previous ones

본 논문에서는 퍼지 추론 시스템의 추론 정보를 이용하여 자율적으로 구조를 결정하는 클러스터링 기법을 제안한다. 제안된 기법은 주어진 입출력 데이터를 이용하여 자율적으로 클러스터의 수를 추정하고 동시에 이들 파라미터를 최적화한다. 일반적인 클러스터링 기법에서 볼 수 있었던 비교사학습을 교사학습으로 확장하여 클러스터 추정에 입출력 인과 관계를 고려한 학습을 실시하게 하여 전체 모델의 성능을 개선하고자 하였다. 출력 정보가 입력공간에서 클러스터링 학습에 적용됨으로써 클러스터링에서의 각 클래스의 구분 작업이 더 원활하게 이루어 질 수 있다. 모의실험을 통하여 기존의 연구 결과와 비교하여 제안된 기법의 유용성을 보인다.

Keywords

References

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