方法证据记录
Online K-means
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.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Online K-means Clustering (Sequential / Streaming K-means)
分类方法记录 · ml-model / machine-learning
- 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. · URL
- Sculley, D. (2010). Web-scale k-means clustering. In Proceedings of the 19th International Conference on World Wide Web (WWW 2010), pp. 1177–1178. ACM. · DOI 10.1145/1772690.1772862
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