방법 비교

	강건 K-평균 군집화	혼합 모형화
분야	통계학	통계학
계열	Latent structure	Latent structure
기원 연도	1997	1894
창시자	Cuesta-Albertos, Gordaliza & Matrán	Karl Pearson
유형	Robust partitional clustering	Latent variable / density estimation
원전	Cuesta-Albertos, J. A., Gordaliza, A., & Matrán, C. (1997). Trimmed k-means: An attempt to robustify quantizers. The Annals of Statistics, 25(2), 553–576.	McLachlan, G. J. & Peel, D. (2000). Finite Mixture Models. Wiley-Interscience. ISBN: 978-0471006268
별칭	trimmed k-means, TCLUST k-means, contamination-resistant k-means, outlier-robust clustering	finite mixture model, mixture distribution model, FMM, model-based clustering
관련	4	6
요약	Robust K-means clustering is an extension of classical k-means that protects cluster estimates from distortion caused by outliers or contaminated observations. By trimming a user-specified fraction of the most extreme points before updating cluster centers, the algorithm yields stable, meaningful partitions even when the data contain atypical cases that would severely bias standard k-means.	Mixture modeling assumes that a population is composed of K unobserved subpopulations, each described by its own probability distribution. The observed data are treated as draws from a weighted combination of these component distributions. It provides a principled, model-based alternative to ad hoc clustering and supports formal comparison of solutions with different numbers of components.