विधियों की तुलना करें

	ऑनलाइन के-मीन्स	के-मीन्स क्लस्टरिंग
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	1967 (online update rule); 2010 (mini-batch variant)	1967
प्रवर्तक	MacQueen, J. (batch); Sculley, D. (mini-batch web-scale variant)	MacQueen, J.
प्रकार	Unsupervised clustering (online/streaming)	Partitional clustering (centroid-based)
मौलिक स्रोत	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.	MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297.
उपनाम	sequential k-means, streaming k-means, incremental k-means, online clustering	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering
संबंधित	4	3
सारांश	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.	K-Means Clustering is a centroid-based partitional clustering algorithm, traced to J. MacQueen in 1967, that splits data into k clusters by assigning each observation to its nearest cluster centre. It is widely used for marketing segmentation, customer grouping, and exploratory analysis.