Improving interpretability of multivariate data through rotations of artificial variates
  • 황선영
  • 박애미
Citations

WEB OF SCIENCE

0
Citations

SCOPUS

0

초록

It is usual that multivariate data analysis produces related (small number of) artificial variates for data reduction. Among them, refer to MDS(multidimensional scaling), MDPREF(multidimensional preference analysis), CDA(canonical discriminant analysis), CCA(canonical correlation analysis) and FA(factor analysis). Varimax rotation of artificial variables which is originally invented in FA for easy interpretations is applied to diverse multivariate techniques mentioned above. Real data analysisis is performed in order to manifest that rotation improves interpretations of artificial variables.

키워드

Artificial variableInterpretationVarimax rotationArtificial variableInterpretationVarimax rotation
제목
Improving interpretability of multivariate data through rotations of artificial variates
저자
황선영박애미
발행일
2004-06
저널명
한국데이터정보과학회지
15
2
페이지
297 ~ 306