Bandingkan metode

	Robust k-means	Clustering K-means
Bidang	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning
Tahun asal	1999	1967 (formalized 1982)
Pencetus	Garcia-Escudero, L. A. & Gordaliza, A.	MacQueen, J. B.; Lloyd, S. P.
Tipe	Robust clustering algorithm	Partitional clustering
Sumber perintis	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.
Alias	robust k-means clustering, trimmed k-means, outlier-resistant k-means, RKM	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
Terkait	4	4
Ringkasan	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.