手法を比較

	ロバストk平均法	階層的クラスタリング	K-means クラスタリング	スペクトラルクラスタリング
分野	機械学習	機械学習	機械学習	機械学習
系統	Machine learning	Machine learning	Machine learning	Machine learning
提唱年	1999	1963	1967 (formalized 1982)	2002
提唱者	Garcia-Escudero, L. A. & Gordaliza, A.	Ward, J. H.	MacQueen, J. B.; Lloyd, S. P.	Ng, A. Y.; Jordan, M. I.; Weiss, Y.
種類	Robust clustering algorithm	Unsupervised clustering (agglomerative)	Partitional clustering	Graph-based clustering (spectral method)
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.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.	Ng, A. Y., Jordan, M. I., & Weiss, Y. (2002). On Spectral Clustering: Analysis and an Algorithm. Advances in Neural Information Processing Systems, 14, 849–856.
別名	robust k-means clustering, trimmed k-means, outlier-resistant k-means, RKM	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means	NJW spectral clustering, graph Laplacian clustering, normalized spectral clustering, spectral graph clustering
関連	4	4	4	5
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.	Hierarchical clustering is an unsupervised method that groups observations into nested clusters and draws the result as a dendrogram, so the number of clusters need not be fixed in advance. Its agglomerative form rests on the objective-function grouping criterion introduced by Joe Ward in 1963.	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.	Spectral Clustering is a graph-based unsupervised learning algorithm, formalized by Ng, Jordan, and Weiss in 2002, that maps data points into a low-dimensional eigenspace derived from the similarity graph's Laplacian before applying k-means. This spectral embedding makes it possible to recover clusters of arbitrary shape — rings, crescents, interleaved spirals — that Euclidean distance-based methods consistently fail to separate.