Machine learningMachine learning
K-means Clustering
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.
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Sources
- Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI: 10.1109/TIT.1982.1056489 ↗
- 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. link ↗
Related methods
Referenced by
Apriori AlgorithmAssociation RulesAutoencoderBayesian Gaussian Mixture ModelBIRCHDTW Barycenter AveragingEnsemble HDBSCANEnsemble K-meansExplainable DBSCANMean ShiftOnline Gaussian Mixture ModelRegularized Gaussian Mixture ModelRegularized k-meansRobust Gaussian Mixture ModelRobust HDBSCANRobust k-meansSelf-supervised DBSCANSelf-supervised K-meansSemi-supervised DBSCANSemi-supervised HDBSCANSemi-supervised K-meansSpectral ClusteringUMAP