So sánh phương pháp
Xem các phương pháp đã chọn cạnh nhau; những hàng khác biệt được làm nổi bật.
| Mô hình Khối Ngẫu nhiên (Stochastic Block Model - SBM)× | Phân cụm K-Means× | |
|---|---|---|
| Lĩnh vực≠ | Phân tích mạng lưới | Học máy |
| Họ≠ | Process / pipeline | Machine learning |
| Năm ra đời≠ | 1983 | 1967 |
| Người khởi xướng≠ | — | MacQueen, J. |
| Loại≠ | Probabilistic generative graph model | Partitional clustering (centroid-based) |
| Công trình gốc≠ | Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137. DOI ↗ | 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 ↗ |
| Tên gọi khác | SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM) | K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering |
| Liên quan≠ | 7 | 3 |
| Tóm tắt≠ | The Stochastic Block Model (SBM), introduced by Holland, Laskey and Leinhardt (1983), is a probabilistic generative model for graphs that assigns nodes to latent blocks and parametrically estimates the connection probabilities between blocks. It is the foundational approach for community detection, core-periphery identification, and hierarchical structure discovery in network analysis. | 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. |
| ScholarGateBộ dữ liệu ↗ |
|
|