응용통계연구 (The Korean Journal of Applied Statistics)

  • 제26권5호
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  • Pages.821-834
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  • 2013
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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정규분포기반 두각 혼합모형의 순환적 적합을 이용한 군집분석에서의 변수선택

Variable Selection in Clustering by Recursive Fit of Normal Distribution-based Salient Mixture Model

  • 김승구 (상지대학교 컴퓨터데이터정보학과)
  • Kim, Seung-Gu (Department of Data and Information, Sangji University)
  • 투고 : 2013.08.26
  • 심사 : 2013.10.21
  • 발행 : 2013.10.31
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⟨ 이전 논문 다음 논문 ⟩

초록

Law 등 (2004)은 군집분석에서 변수선택을 위해 정규분포기반 "두각 혼합모형(salient mixture model)"의 사용을 제안하였다. 본 논문에서는 이 모형의 적합 상의 문제점과 변수선택의 결함을 지적하고 그 대안을 제시한다. 모의자료와 실자료를 바탕으로 제안된 방법이 기존의 방법보다 유용함을 보였다.

Law et al. (2004) proposed a normal distribution based salient mixture model for variable selection in clustering. However, this model has substantial problems such as the unidentifiability of components an the inaccurate selection of informative variables in the case of a small cluster size. We propose an alternative method to overcome problems and demonstrate a good performance through experiments on simulated data and real data.

키워드

참고문헌

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