Methoden vergleichen

	Online DBSCAN	Online K-means
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	1998	1967 (online update rule); 2010 (mini-batch variant)
Urheber	Ester, M., Kriegel, H.-P., Sander, J., Wimmer, M., & Xu, X.	MacQueen, J. (batch); Sculley, D. (mini-batch web-scale variant)
Typ	Incremental density-based clustering	Unsupervised clustering (online/streaming)
Wegweisende Quelle	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.	MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, pp. 281–297. University of California Press.
Aliasnamen	Incremental DBSCAN, Streaming DBSCAN, Online density-based clustering, iDBSCAN	sequential k-means, streaming k-means, incremental k-means, online clustering
Verwandt	5	4
Zusammenfassung	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 K-means is a streaming variant of the classical K-means algorithm that updates cluster centroids one observation at a time — or in small mini-batches — without storing the entire dataset in memory. It is particularly suited to large-scale, real-time, or continuously arriving data where batch recomputation would be too slow or impractical.