পদ্ধতির তুলনা করুন

	Online DBSCAN	HDBSCAN	অনলাইন গাউসিয়ান মিক্সচার মডেল
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning	Machine learning
উদ্ভবের বছর	1998	2013	2000–2009
প্রবর্তক	Ester, M., Kriegel, H.-P., Sander, J., Wimmer, M., & Xu, X.	Campello, R. J. G. B.; Moulavi, D.; Sander, J.	Cappé, O. & Moulines, E. (online EM formulation)
ধরন	Incremental density-based clustering	Hierarchical density-based clustering	Probabilistic clustering / density estimation (incremental)
মৌলিক উৎস	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.	Campello, R. J. G. B., Moulavi, D., & Sander, J. (2013). Density-Based Clustering Based on Hierarchical Density Estimates. In J. Pei et al. (Eds.), Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science, vol. 7819 (pp. 160–172). Springer, Berlin, Heidelberg.	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.
অপর নাম	Incremental DBSCAN, Streaming DBSCAN, Online density-based clustering, iDBSCAN	HDBSCAN, Hierarchical DBSCAN, hierarchical density-based clustering, HDBSCAN*	Online GMM, Incremental GMM, Streaming Gaussian Mixture Model, Sequential GMM
সম্পর্কিত	5	3	5
সারসংক্ষেপ	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.	HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm introduced by Campello, Moulavi, and Sander in 2013. It extends DBSCAN by building a full hierarchy of density-based clusters across all density scales and then extracting a stable flat partition, making it robust to datasets where cluster densities vary substantially across regions.	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.