قارن الطرق

	التجميع الضبابي C-Means (FCM)	تجميع العنقودية باستخدام المتوسطات (K-Means Clustering)
المجال	تعلم الآلة	تعلم الآلة
العائلة	Machine learning	Machine learning
سنة النشأة	1981	1967
صاحب الطريقة	Joseph Dunn; James Bezdek	MacQueen, J.
النوع	Soft (fuzzy) partitional clustering	Partitional clustering (centroid-based)
المصدر التأسيسي	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.	MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297.
الأسماء البديلة	FCM, fuzzy clustering, soft k-means, bulanık c-ortalama kümeleme	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering
ذات صلة	3	3
الملخص	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.	K-Means Clustering is a centroid-based partitional clustering algorithm, traced to J. MacQueen in 1967, that splits data into k clusters by assigning each observation to its nearest cluster centre. It is widely used for marketing segmentation, customer grouping, and exploratory analysis.