치의학 연구에서 이상치의 처리

Outlier detection in dental research

  • 김기열 (연세대학교 치과대학 BK21 플러스 통합구강생명과학 사업단, 치의학 교육연구센터)
  • Kim, Ki-Yeol (BK21 PLUS Project, Dental Education Research Center, Yonsei University College of Dentistry)
  • 투고 : 2017.05.11
  • 심사 : 2017.06.30
  • 발행 : 2017.09.30

초록

In clinical dental research, errors occur in spite of careful study design and conduct. Data cleaning procedures intend to identify and correct these errors or at least to minimize their influence on study. Outlier is the one of these errors. Outlier detection is the first step in data analysis process which has a serious effect in the field of dental research. Hence, this paper aims to introduce the methods to detect the outliers and to examine their influences in statistical data analysis.

키워드

과제정보

연구 과제 주관 기관 : 연세대학교 치과대학

참고문헌

  1. CrowdFlower, Data Science. 2016.
  2. Yun, S.-C., Imputation of Missing Values. J Prev Med Public Health, 2004. 37(3): p. 209-211.
  3. Graham Williams, R.B., Hongxing He, Simon Hawkins and Lifang Gu, A Comparative Study of RNN for Outlier Detection in Data Mining, in CSIRO Technical Report. 2002, CSIRO.
  4. Hancong Liu, S.S., Wei Jiang, On-line outlier detection and data cleaning. Computers & Chemical Engineering, 2004. 28(9): p. 1635-1647. https://doi.org/10.1016/j.compchemeng.2004.01.009
  5. Tukey, J.W., Exploratory data analysis. 1977.
  6. Grubbs, F.E., Procedures for detecting outlying observations in samples. Technometrics 1969. 11(1): p. 1-21. https://doi.org/10.1080/00401706.1969.10490657
  7. Ray, S. A Comprehensive Guide to Data Exploration. Available from: https://www.analyticsvidhya.com/blog/2016/01/guide-data-exploration/#two.
  8. V. Deneshkumar, K.S., M. Manikandan, Identification of Outliers in Medical Diagnostic System Using Data Mining Techniques. International Journal of Statistics and Applications, 2014. 4(6): p. 241-248.
  9. 임회정, SPSS 를 이용한 치의학 통계 입문 및 자료분석, 2008. 나래출판사.