Compară metode

	Reguli de asociere semi-supervizate	Propagarea etichetelor
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	2003–2010s	2002
Autorul original	Liu, B.; Hsu, W.; Ma, Y. (and subsequent researchers)	Zhu, X. & Ghahramani, Z.
Tip	Pattern mining with partial supervision	Graph-based semi-supervised classification
Sursa seminală	Liu, B., Hsu, W., & Ma, Y. (2003). Integrating Classification and Association Rule Mining. In Proceedings of the 4th IEEE International Conference on Data Mining (ICDM), pp. 339–346.	Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.
Denumiri alternative	semi-supervised ARM, label-guided association rule mining, constrained association rule mining, semi-supervised pattern discovery	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation
Înrudite	4	3
Rezumat	Semi-supervised association rule mining extends classical association rule learning by incorporating a small amount of labeled data alongside a larger unlabeled dataset. It uses known class information or user-provided constraints to guide the discovery of rules that are both statistically frequent and semantically meaningful, bridging unsupervised pattern mining with light supervision.	Label Propagation is a graph-based semi-supervised learning algorithm introduced by Zhu and Ghahramani in 2002 that spreads class labels from a small set of labeled nodes to a large set of unlabeled nodes by iteratively diffusing label information along the edges of a similarity graph, exploiting the manifold structure of the data.