Comparar métodos

	HDBSCAN semi-supervisionado	Modelo Gaussiano de Mistura Semi-supervisionado
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	2017–present	2000
Autor original	McInnes, L.; Healy, J. (base HDBSCAN); semi-supervised extensions by various authors	Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T.
Tipo	Semi-supervised density-based clustering	Generative semi-supervised classifier
Fonte seminal	McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Outros nomes	Constrained HDBSCAN, Semi-supervised hierarchical density clustering, HDBSCAN with partial labels, SS-HDBSCAN	SS-GMM, semi-supervised GMM, partially labeled Gaussian mixture model, generative semi-supervised classifier
Relacionados	6	3
Resumo	Semi-supervised HDBSCAN extends the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm by incorporating partial supervision — such as must-link and cannot-link pairwise constraints or a small set of labeled examples — to guide the density-based cluster hierarchy toward cluster assignments that are consistent with available domain knowledge.	The Semi-supervised Gaussian Mixture Model (SS-GMM) is a generative probabilistic classifier that fits a Gaussian mixture to both labeled and unlabeled data using the Expectation-Maximization algorithm. Labeled points constrain component assignments while unlabeled points improve density estimates, enabling effective learning when annotations are scarce.