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Method evidence record

K-means

K-means is a classic unsupervised partitional clustering algorithm that divides a dataset into K non-overlapping groups by iteratively assigning each observation to its nearest centroid and updating centroids as the mean of their assigned points. It is one of the most widely used exploratory tools in machine learning and data analysis.

Evidence statusSources recorded, not reviewedBibliographic sources are present. Claim-level evidence review has not been performed.

Source record

Recorded

Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.

K-means Clustering Algorithm

Taxonomic method recordml-modelmachine-learning

  1. Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. · DOI 10.1109/TIT.1982.1056489

  2. MacQueen, J. B. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297. · URL

Open full method2 recorded citations, copied from the method source record.

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Related methods

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