Сравнение методов

	Grey Clustering: Классификация на основе отбеливания в условиях неопределенности	Нечеткая кластеризация C-средних (FCM)	Модель серого прогнозирования GM(1,1)
Область	Мягкие вычисления	Машинное обучение	Мягкие вычисления
Семейство	Machine learning	Machine learning	Regression model
Год появления	2010	1981	1982
Автор метода	Julong Deng; Sifeng Liu	Joseph Dunn; James Bezdek	Julong Deng
Тип	Whitenization-based soft clustering	Soft (fuzzy) partitional clustering	Small-sample grey forecasting model
Основополагающий источник	Liu, S., & Lin, Y. (2010). Grey Systems: Theory and Applications. Springer. ISBN: 978-3-642-13937-6	Dunn, J. C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 3(3), 32–57.	Deng, J. L. (1982). Control problems of grey systems. Systems & Control Letters, 1(5), 288–294.
Другие названия	Grey Whitenization Weight Function Clustering, Grey Fixed-Weight Clustering, Grey Variable-Weight Clustering, Gri Kümeleme	FCM, fuzzy clustering, soft k-means, bulanık c-ortalama kümeleme	GM(1,1), grey prediction model, grey forecasting, gri tahmin modeli
Связанные	2	3	2
Сводка	Grey Clustering is a classification method from grey systems theory that assigns objects to predefined grey classes using whitenization weight functions. Developed within the framework of Deng Julong's grey system theory and systematized by Sifeng Liu, it is particularly suited for situations involving small sample sizes, incomplete information, or uncertain data—conditions common in engineering assessments, environmental monitoring, and socioeconomic evaluation. The method quantifies how strongly each object belongs to each grey class and makes a crisp assignment based on maximum clustering coefficients.	Fuzzy C-Means is a soft clustering algorithm in which every data point belongs to every cluster with a graded membership between 0 and 1, rather than being assigned to exactly one cluster. Originated by Joseph Dunn in 1973 and generalized by James Bezdek in 1981, it minimizes a fuzzy-weighted within-cluster variance, making it well suited to data whose groups overlap or have no sharp boundaries.	GM(1,1) is the core forecasting model of grey system theory, introduced by Julong Deng in 1982, designed to predict from very few observations and incomplete information — situations where classical time-series models like ARIMA need far more data. It accumulates the raw series to expose a hidden exponential trend, fits a first-order grey differential equation, and projects future values, making it popular in engineering, energy, and management forecasting with short data records.