Examining DETECT"s Performance in finding Dimensionally Distinctive Clusters in Comparison with Other Clustering Methods

• 영문명: Examining DETECT"s Performance in finding Dimensionally Distinctive Clusters in Comparison with Other Clustering Methods
• 발행기관: 상지대학교 환경과학기술연구소
• 저자명: Hae Rim Kim
• 간행물 정보: 『환경과학연구』제12권 제1호, 37~42쪽, 전체 6쪽
• 주제분류: 공학 > 환경공학
• 파일형태: PDF
• 발행일자: 2006.12.30

영문 초록

  A data dimension analysis tool, DETECT, was compared with factor analysis, discrimination and classification analysis, and cluster analysis in both simulation and real data study, especially for small size sample. In simulation DETECT"s recovering rate is slightly smaller than in factor analysis. But in real data study both results resemble each other much and have acceptable meanings. Discrimination and classification analysis performs well with simulated data, but it has limit that with real data because cluster structure is latent. Results from cluster analysis were not strong enough as in other three procedures. It is contemplated that cluster analysis is" very sensitive to noise, hence partial points might be considered as a noise not as information useful. Overall both factor analysis and DETECT procedure are compatible and reliable in educational/psychometric data scored in graded manner. It is useful to refer to both procedures" results together to make analysis more reliable and valid.

목차

Abstract
1. Introduction
2. DETECT
3. Simulation Study
4. A Real Data Study
5. Closing Remarks
References

키워드

DETECT factor analysis discrimination and classification cluster analysis