Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Кластеризація методом k-середніх× | Лінійний дискримінантний аналіз (LDA× | |
|---|---|---|
| Галузь≠ | Машинне навчання | Статистика |
| Родина≠ | Machine learning | Hypothesis test |
| Рік появи≠ | 1967 | 1936 |
| Автор методу≠ | MacQueen, J. | Ronald A. Fisher |
| Тип≠ | Partitional clustering (centroid-based) | Parametric linear classifier / dimensionality reduction |
| Основоположне джерело≠ | 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 ↗ |
| Інші назви≠ | K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering | LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis |
| Пов'язані≠ | 3 | 7 |
| Підсумок≠ | 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. |
| ScholarGateНабір даних ↗ |
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