방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 확률적 블록 모형 (Stochastic Block Model, SBM)× | K-평균 군집화× | |
|---|---|---|
| 분야≠ | 네트워크 분석 | 머신러닝 |
| 계열≠ | Process / pipeline | Machine learning |
| 기원 연도≠ | 1983 | 1967 |
| 창시자≠ | — | MacQueen, J. |
| 유형≠ | Probabilistic generative graph model | Partitional clustering (centroid-based) |
| 원전≠ | 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 ↗ |
| 별칭 | SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM) | K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering |
| 관련≠ | 7 | 3 |
| 요약≠ | 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. |
| ScholarGate데이터셋 ↗ |
|
|