Võrdle meetodeid

	Võrgus DBSCAN	Online Gaussi segamudel
Valdkond	Masinõpe	Masinõpe
Perekond	Machine learning	Machine learning
Tekkeaasta	1998	2000–2009
Looja	Ester, M., Kriegel, H.-P., Sander, J., Wimmer, M., & Xu, X.	Cappé, O. & Moulines, E. (online EM formulation)
Tüüp	Incremental density-based clustering	Probabilistic clustering / density estimation (incremental)
Algallikas	Ester, M., Kriegel, H.-P., Sander, J., Wimmer, M., & Xu, X. (1998). Incremental Clustering for Mining in a Data Warehousing Environment. In Proceedings of the 24th International Conference on Very Large Data Bases (VLDB), pp. 323–333.	Cappé, O. & Moulines, E. (2009). On-line expectation-maximization algorithm for latent data models. Journal of the Royal Statistical Society: Series B, 71(3), 593–613.
Rööpnimetused	Incremental DBSCAN, Streaming DBSCAN, Online density-based clustering, iDBSCAN	Online GMM, Incremental GMM, Streaming Gaussian Mixture Model, Sequential GMM
Seotud	5	5
Kokkuvõte	Online DBSCAN extends the classic density-based clustering algorithm to handle continuously arriving data points without re-clustering the entire dataset from scratch. Each new observation is integrated into the existing cluster structure by local neighborhood queries, making it practical for streaming and data-warehousing scenarios where data grows incrementally.	Online Gaussian Mixture Model adapts the classic GMM to streaming or large-scale data by replacing full-batch EM with incremental updates — processing one observation or mini-batch at a time and continuously refining component means, covariances, and mixing weights without revisiting the entire dataset.