Salīdzināt metodes

	Robust K-means Clustering	Jaukto sadalījumu modelēšana
Nozare	Statistika	Statistika
Saime	Latent structure	Latent structure
Izcelsmes gads	1997	1894
Autors	Cuesta-Albertos, Gordaliza & Matrán	Karl Pearson
Tips	Robust partitional clustering	Latent variable / density estimation
Pirmavots	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
Citi nosaukumi	trimmed k-means, TCLUST k-means, contamination-resistant k-means, outlier-robust clustering	finite mixture model, mixture distribution model, FMM, model-based clustering
Saistītās	4	6
Kopsavilkums	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.