Earticle

초록

영어

We consider the Fuzzy clustering which is devised for partitioning a set of objects into a certain number of groups by assigning the membership probabilities to each object. The researches carried out in this field before show that the Fuzzy clustering concept is involved so much that for a certain set of data, the main purpose of the clustering cannot be attained as desired. Thus we propose a new objective function, named as Fuzzy-Entroppy Function in order to satisfy the main motivation of the clustering which is classifying the data clearly. Also we suggest Mean Field Annealing Algorithm as an optimization algorithm rather than the. ISODATA used traditionally in this field since the objective function is changed. We show the Mean Field Annealing Algorithm works pretty well not only for the new objective function but also for the classical Fuzzy objective function by indicating that the local minimum problem resulted from the ISODATA can be improved.

목차

I. 서론
 II. Fuzzy 집락분석
  1. Fuzzy 집락분석의 정의
  2. Fuzzy ISODATA 알고리즘
 III. Fuzzy-Entropy 집락분석
  1. Fuzzy-Entropy 집락분석을 위한 목적함수
  2. Fuzzy-Entropy 집락분석 알고리즘의 배경
  3. 최적화 연구
 IV. 비교 
  1. 뭉쳐있는 개체들에 대하여 방법 1, 2, 3을 사용하여 두 집락으로 분류
  2. λ값에 따른 변화
  3. MFA방법으로 수행한 결과와 고전적인 K-means방법과의 비교
 V. 결론
 참고문헌
 Abstract

저자

• 임대혁 [ Im, Dae-heug | 혜천대학 경영정보시스템 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  경영과 정보연구 [Management Information Systems review]

• 간기

  계간

• pISSN

  1598-2459

• 수록기간

  1997~2025

• 등재여부

  KCI 등재

• 십진분류

  KDC 325 DDC 658