مقایسهٔ روش‌ها

	خوشه‌بندی K-means مقاوم (Robust K-means Clustering)	تحلیل خوشه‌ای
حوزه	آمار	آمار
خانواده	Latent structure	Latent structure
سال پیدایش	1997	1939–1967
پدیدآور	Cuesta-Albertos, Gordaliza & Matrán	Robert C. Tryon (early development); Ward (1963) for hierarchical; MacQueen (1967) for k-means
نوع	Robust partitional clustering	Unsupervised classification / grouping
منبع بنیادین	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.	Everitt, B. S., Landau, S., Leese, M. & Stahl, D. (2011). Cluster Analysis (5th ed.). Wiley. ISBN: 978-0470749913
نام‌های دیگر	trimmed k-means, TCLUST k-means, contamination-resistant k-means, outlier-robust clustering	clustering, unsupervised classification, data clustering, numerical taxonomy
مرتبط	4	5
خلاصه	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.	Cluster analysis is a family of unsupervised multivariate techniques that partition a set of objects or observations into internally homogeneous, mutually distinct groups — clusters — based on measured characteristics, without any prior knowledge of group membership. It is widely used in market segmentation, bioinformatics, psychology, and social science to reveal natural groupings in data.