Compara mètodes

	Agrupació Difusa C-Mitjanes (FCM)	Computació granular (Granulació d'informació)	Agrupació K-Means
Camp	Aprenentatge automàtic	Computació tova	Aprenentatge automàtic
Família	Machine learning	Machine learning	Machine learning
Any d'origen	1981	1997	1967
Autor original	Joseph Dunn; James Bezdek	Lotfi A. Zadeh (information granulation); developed by Pedrycz, Skowron, Yao	MacQueen, J.
Tipus	Soft (fuzzy) partitional clustering	Framework for multi-granularity information processing	Partitional clustering (centroid-based)
Font seminal	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.	Zadeh, L. A. (1997). Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems, 90(2), 111–127.	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.
Àlies	FCM, fuzzy clustering, soft k-means, bulanık c-ortalama kümeleme	information granulation, computing with granules, three-way granular computing, tanecikli hesaplama	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering
Relacionats	3	3	3
Resum	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.	Granular computing is a problem-solving paradigm that processes information in 'granules' — clumps of objects drawn together by indistinguishability, similarity, or functionality — rather than at the level of individual data points. Articulated by Lotfi Zadeh in 1997 as fuzzy information granulation and developed into a broad framework, it provides a unifying umbrella over fuzzy sets, rough sets, and interval methods, letting analysis move to whichever level of detail a problem actually requires.	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.