So sánh phương pháp

	Online DBSCAN	DBSCAN	Mô hình hỗn hợp Gauss trực tuyến
Lĩnh vực	Học máy	Học máy	Học máy
Họ	Machine learning	Machine learning	Machine learning
Năm ra đời	1998	1996	2000–2009
Người khởi xướng	Ester, M., Kriegel, H.-P., Sander, J., Wimmer, M., & Xu, X.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Cappé, O. & Moulines, E. (online EM formulation)
Loại	Incremental density-based clustering	Density-based clustering algorithm	Probabilistic clustering / density estimation (incremental)
Công trình gốc	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.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd KDD, 226–231.	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.
Tên gọi khác	Incremental DBSCAN, Streaming DBSCAN, Online density-based clustering, iDBSCAN	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	Online GMM, Incremental GMM, Streaming Gaussian Mixture Model, Sequential GMM
Liên quan	5	3	5
Tóm tắt	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.	DBSCAN is a density-based clustering algorithm, introduced by Ester, Kriegel, Sander and Xu in 1996, that groups together points lying in dense regions and flags points in sparse regions as noise. It is effective on noisy data and on clusters of irregular, non-spherical shapes.	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.