So sánh phương pháp
Xem các phương pháp đã chọn cạnh nhau; những hàng khác biệt được làm nổi bật.
| Phân cụm K-means× | Phân tích thành phần chính× | |
|---|---|---|
| Lĩnh vực | Học máy | Học máy |
| Họ | Machine learning | Machine learning |
| Năm ra đời≠ | 1967 (formalized 1982) | 2002 |
| Người khởi xướng≠ | MacQueen, J. B.; Lloyd, S. P. | Jolliffe, I.T. (textbook); Pearson & Hotelling (origins) |
| Loại≠ | Partitional clustering | Unsupervised dimensionality reduction |
| Công trình gốc≠ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ | Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer. DOI ↗ |
| Tên gọi khác | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means | Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform |
| Liên quan≠ | 4 | 3 |
| Tóm tắt≠ | 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. | Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures. |
| ScholarGateBộ dữ liệu ↗ |
|
|