Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Agrupamento K-means× | Agrupamento Hierárquico× | |
|---|---|---|
| Área | Aprendizado de máquina | Aprendizado de máquina |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 1967 (formalized 1982) | 1963 |
| Autor original≠ | MacQueen, J. B.; Lloyd, S. P. | Ward, J. H. |
| Tipo≠ | Partitional clustering | Unsupervised clustering (agglomerative) |
| Fonte seminal≠ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ | Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244. DOI ↗ |
| Outros nomes≠ | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means | Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering |
| Relacionados | 4 | 4 |
| Resumo≠ | 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. | Hierarchical clustering is an unsupervised method that groups observations into nested clusters and draws the result as a dendrogram, so the number of clusters need not be fixed in advance. Its agglomerative form rests on the objective-function grouping criterion introduced by Joe Ward in 1963. |
| ScholarGateConjunto de dados ↗ |
|
|