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| Клъстериране с К-средни× | Линеен дискриминантен анализ (LDA× | |
|---|---|---|
| Област≠ | Машинно обучение | Статистика |
| Семейство≠ | Machine learning | Hypothesis test |
| Година на възникване≠ | 1967 | 1936 |
| Създател≠ | MacQueen, J. | Ronald A. Fisher |
| Тип≠ | Partitional clustering (centroid-based) | Parametric linear classifier / dimensionality reduction |
| Основополагащ източник≠ | MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297. link ↗ | Fisher, R.A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179–188. DOI ↗ |
| Други названия≠ | K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering | LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis |
| Свързани≠ | 3 | 7 |
| Резюме≠ | K-Means Clustering is a centroid-based partitional clustering algorithm, traced to J. MacQueen in 1967, that splits data into k clusters by assigning each observation to its nearest cluster centre. It is widely used for marketing segmentation, customer grouping, and exploratory analysis. | Linear Discriminant Analysis (LDA) is a parametric supervised classification method that finds the linear combination of continuous predictors that best separates two or more predefined groups. Introduced by Ronald A. Fisher in his landmark 1936 paper on taxonomic measurements, it simultaneously serves as a classifier and a dimensionality-reduction tool, and can be understood as the classification-oriented counterpart of MANOVA. |
| ScholarGateНабор от данни ↗ |
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