Journal of Internet Computing and Services (인터넷정보학회논문지)

Korean Society for Internet Information (한국인터넷정보학회)
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New Sequential Clustering Combination for Rule Generation System

규칙 생성 시스템을 위한 새로운 연속 클러스터링 조합

  • Kim, Sung Suk (Computer Science, Korea Advanced Institute of Science and Technology) ;
  • Choi, Ho Jin (Computer Science, Korea Advanced Institute of Science and Technology)
  • Received : 2012.04.26
  • Accepted : 2012.08.02
  • Published : 2012.10.31
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Abstract

In this paper, we propose a new clustering combination based on numerical data driven for rule generation mechanism. In large and complicated space, a clustering method can obtain limited performance results. To overcome the single clustering method problem, hybrid combined methods can solve problem to divided simple cluster estimation. Fundamental structure of the proposed method is combined by mountain clustering and modified Chen clustering to extract detail cluster information in complicated data distribution of non-parametric space. It has automatic rule generation ability with advanced density based operation when intelligent systems including neural networks and fuzzy inference systems can be generated by clustering results. Also, results of the mechanism will be served to information of decision support system to infer the useful knowledge. It can extend to healthcare and medical decision support system to help experts or specialists. We show and explain the usefulness of the proposed method using simulation and results.

본 논문에서는 수치적 데이터를 이용하여 규칙을 생성하는 시스템에 대해 순차적인 클러스터링 방법을 제안한다. 단일 클러스터링 기법은 방대하고 복잡한 공간 내에서는 원하는 결과를 얻지 못할 수 있다. 이런 문제점을 해결하기 위해 제안된 방법은 서로 다른 클러스터링 기법을 순차적으로 수행하여 장점들은 활용하고 단점들은 보안하는 형태를 제안하였다. Mountain 클러스터링과 Chen 클러스터링을 이용하여 non-parametric 공간에서 자율적으로 클러스터를 구성하였고, global 공간과 local 공간으로 역할을 분담하여 클러스터를 추정한다. 추정된 클러스터들은 신경회로망이나 퍼지 시스템과 같은 지능 시스템의 구조와 초기 파라미터 결정에 활용될 수 있으며, 확장하여 헬스케어와 의료 분야에서의 결정 제공 시스템의 학습에 도움을 줄 수 있다. 제안된 방법을 유용성을 시뮬레이션을 통해 보이고자 한다.

Keywords

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