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| Факторен анализ× | K-means клъстеризация× | |
|---|---|---|
| Област≠ | Статистика за изследвания | Машинно обучение |
| Семейство≠ | Process / pipeline | Machine learning |
| Година на възникване≠ | 1931 | 1967 (formalized 1982) |
| Създател≠ | Louis Leon Thurstone | MacQueen, J. B.; Lloyd, S. P. |
| Тип≠ | Method | Partitional clustering |
| Основополагащ източник≠ | Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press. DOI ↗ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ |
| Други названия≠ | EFA, CFA, latent variable modeling | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means |
| Свързани≠ | 3 | 4 |
| Резюме≠ | Factor analysis is a statistical technique for identifying latent (unobserved) dimensions underlying observed variables, developed by Louis Leon Thurstone in the 1930s and formalized by Jöreskog (1969). Exploratory factor analysis (EFA) discovers unknown factor structure from data; confirmatory factor analysis (CFA) tests hypothesized relationships between observed and latent variables. Essential in psychometrics (test development), organizational research (measuring constructs like leadership style), and biomedicine (identifying disease subtypes), factor analysis reduces dimensionality while revealing conceptual organization in multivariate data. | K-means is a classic unsupervised partitional clustering algorithm that divides a dataset into K non-overlapping groups by iteratively assigning each observation to its nearest centroid and updating centroids as the mean of their assigned points. It is one of the most widely used exploratory tools in machine learning and data analysis. |
| ScholarGateНабор от данни ↗ |
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