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| Robuszt K-means klaszterezés× | Klaszteranalízis× | |
|---|---|---|
| Tudományterület | Statisztika | Statisztika |
| Módszercsalád | Latent structure | Latent structure |
| Keletkezés éve≠ | 1997 | 1939–1967 |
| Megalkotó≠ | Cuesta-Albertos, Gordaliza & Matrán | Robert C. Tryon (early development); Ward (1963) for hierarchical; MacQueen (1967) for k-means |
| Típus≠ | Robust partitional clustering | Unsupervised classification / grouping |
| Alapmű≠ | 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. DOI ↗ | Everitt, B. S., Landau, S., Leese, M. & Stahl, D. (2011). Cluster Analysis (5th ed.). Wiley. ISBN: 978-0470749913 |
| Alternatív nevek | trimmed k-means, TCLUST k-means, contamination-resistant k-means, outlier-robust clustering | clustering, unsupervised classification, data clustering, numerical taxonomy |
| Kapcsolódó≠ | 4 | 5 |
| Összefoglaló≠ | 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. |
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