Comparer des méthodes

	Informatique granulaire (Granulation de l'information)	Regroupement par K-moyennes
Domaine	Soft computing	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1997	1967
Auteur d'origine	Lotfi A. Zadeh (information granulation); developed by Pedrycz, Skowron, Yao	MacQueen, J.
Type	Framework for multi-granularity information processing	Partitional clustering (centroid-based)
Source fondatrice	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.
Alias	information granulation, computing with granules, three-way granular computing, tanecikli hesaplama	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering
Apparentées	3	3
Résumé	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.