방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 군집 분석× | 잠재 계층 분석(Latent Class Analysis, LCA)× | |
|---|---|---|
| 분야 | 통계학 | 통계학 |
| 계열 | Latent structure | Latent structure |
| 기원 연도≠ | 1939–1967 | 1950s–1968 |
| 창시자≠ | Robert C. Tryon (early development); Ward (1963) for hierarchical; MacQueen (1967) for k-means | Paul F. Lazarsfeld |
| 유형≠ | Unsupervised classification / grouping | Latent variable / person-centered classification |
| 원전≠ | Everitt, B. S., Landau, S., Leese, M. & Stahl, D. (2011). Cluster Analysis (5th ed.). Wiley. ISBN: 978-0470749913 | Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231. DOI ↗ |
| 별칭 | clustering, unsupervised classification, data clustering, numerical taxonomy | LCA, latent class model, latent categorical analysis, finite mixture of multinomials |
| 관련≠ | 5 | 6 |
| 요약≠ | Cluster analysis is a family of unsupervised multivariate techniques that partition a set of objects or observations into internally homogeneous, mutually distinct groups — clusters — based on measured characteristics, without any prior knowledge of group membership. It is widely used in market segmentation, bioinformatics, psychology, and social science to reveal natural groupings in data. | Latent class analysis identifies unobserved subgroups — latent classes — within a population by finding patterns of responses across a set of categorical observed indicators. It is the categorical-variable counterpart of cluster analysis, but grounded in an explicit probabilistic model, and is widely used in social, health, and behavioral sciences to discover typologies in survey or diagnostic data. |
| ScholarGate데이터셋 ↗ |
|
|