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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국자료분석학회 Journal of The Korean Data Analysis Society Journal of The Korean Data Analysis Society 제7권 제5호
- 발행연도
- 2005.1
- 수록면
- 1,517 - 1,529 (13page)
이용수
초록· 키워드
오류제보하기
In clustering analysis determining the number of clusters is a very crucial issue but there is no fully general and satisfactory criterion for every clustering algorithm. The model-based clustering methodology, to which we mainly focus our attention has some advantages over the classical heuristic methods in the sense that the Bayesian information criterion(BIC) can be used to find an optimal number of clusters. The BIC is an approximation to the integrated likelihood for selecting a clustering model in Bayesian approach. The regularity conditions for an approximation is not generally satisfied for the finite mixture model, so we need to check the performance of the BIC in both of the cases with the Gaussian and the non-Gaussian distributions. The performance of BIC will be compared via Monte Carlo simulation.
목차
등록된 정보가 없습니다.
참고문헌 (9)
참고문헌 신청
/ 1978 / Estimating the Dimension of a Model 6 : 461 ~ 464
/ 1979 / An Attempt to Define the Nature of the Chemical Diabetes Using a Multidimensional Analysis : 17 ~ 24
/ 1988 / Inference and Applications to Clustering
/ 1995 / Bayes Factors 90 : 773 ~ 795
/ 1995 / Gaussian Parsimonious Clustering Models 28, : 781 ~ 793
/ 1979 / An Attempt to Define the Nature of the Chemical Diabetes Using a Multidimensional Analysis : 17 ~ 24
/ 1988 / Inference and Applications to Clustering
/ 1995 / Bayes Factors 90 : 773 ~ 795
/ 1995 / Gaussian Parsimonious Clustering Models 28, : 781 ~ 793
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