手法を比較

	ロバストk平均法	K-means クラスタリング
分野	機械学習	機械学習
系統	Machine learning	Machine learning
提唱年	1999	1967 (formalized 1982)
提唱者	Garcia-Escudero, L. A. & Gordaliza, A.	MacQueen, J. B.; Lloyd, S. P.
種類	Robust clustering algorithm	Partitional clustering
原典	Garcia-Escudero, L. A., & Gordaliza, A. (1999). Robustness properties of k-means and trimmed k-means. Journal of the American Statistical Association, 94(447), 956–969.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
別名	robust k-means clustering, trimmed k-means, outlier-resistant k-means, RKM	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
関連	4	4
概要	Robust k-means is a variant of classical k-means clustering designed to resist the influence of outliers. By trimming a specified fraction of the most extreme observations before computing cluster centers, it produces stable and meaningful partitions even when the data contain noise, contamination, or heavy-tailed distributions — situations where standard k-means breaks down.	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.