IE interfaces (산업공학)

Korean Institute of Industrial Engineers (대한산업공학회)
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An Effective Clustering Procedure for Quantitative Data and Its Application for the Grouping of the Reusable Nuclear Fuel

정량적 자료에 대한 효과적인 군집화 과정 및 사용 후 핵연료의 분류에의 적용

  • Jing, Jin-Xi (Department of Science(Applied Math. Group), Hongik University) ;
  • Yoon, Bok-Sik (Department of Science(Applied Math. Group), Hongik University) ;
  • Lee, Yong-Joo (School of Business Administration, Ewha Women's University)
  • 강금석 (홍익대학교 기초과학과 응용수학) ;
  • 윤복식 (홍익대학교 기초과학과 응용수학) ;
  • 이용주 (이화여대 경영학과)
  • Received : 20020100
  • Accepted : 20020300
  • Published : 2002.06.30
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Abstract

Clustering is widely used in various fields in order to investigate structural characteristics of the given data. One of the main tasks of clustering is to partition a set of objects into homogeneous groups for the purpose of data reduction. In this paper a simple but computationally efficient clustering procedure is devised and some statistical techniques to validate its clustered results are discussed. In the given procedure, the proper number of clusters and the clustered groups can be determined simultaneously. The whole procedure is applied to a practical clustering problem for the classification of reusable fuels in nuclear power plants.

Keywords

References

  1. Baker, F. B. (1975), Measuring the Power of Hierarchical duster Analysis, journal of the American Statistical Association, 70(349),31-38
  2. Chao, A. (1992), Estimating the Number of Classes Via Sample Coverage, journal of the American Statistical Association, 87(417), 210-217
  3. Everitt, B. S. (1991), Applied Multivariate Data Analysis, Wiley, New York
  4. Gordon, A. D. (1994), Identifying genuine clusters in a classification, Computational Statistics & Data Analysis, 18, 561-581 https://doi.org/10.1016/0167-9473(94)90085-X
  5. Hand, D.J. (1981), Discrimination and Classification, Wiley, New York
  6. Jolliffe, I.T. (1995), Identifying influential observations in hierarchical cluster analysis, Journal of Applied Statistics, 22(1), 61-80 https://doi.org/10.1080/757584398
  7. KnanowskiI, W. J.(1995), Recent Advanced in Descriptive Multivariate Analysis, Oxford
  8. Peck, R.., Fisher, I. and Ness, J. V. (1989), Approximate confidence intervals for the number of clusters, Journal of the American Statistical Association, 84 (405), 184-191 https://doi.org/10.2307/2289862