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| 불확실성 하에서의 분류를 위한 백색화 기반 회색 클러스터링× | 퍼지 C-평균 군집화 (FCM)× | |
|---|---|---|
| 분야≠ | 소프트 컴퓨팅 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2010 | 1981 |
| 창시자≠ | Julong Deng; Sifeng Liu | Joseph Dunn; James Bezdek |
| 유형≠ | Whitenization-based soft clustering | Soft (fuzzy) partitional clustering |
| 원전≠ | 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. DOI ↗ |
| 별칭 | 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 |
| 관련≠ | 2 | 3 |
| 요약≠ | 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. |
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