Comparer des méthodes

	L'analyse formelle de concepts (FCA)	Cartes cognitives floues (FCM)	Regroupement par K-moyennes
Domaine	Soft computing	Soft computing	Apprentissage automatique
Famille	Machine learning	Process / pipeline	Machine learning
Année d'origine	1982	1986	1967
Auteur d'origine	Rudolf Wille & Bernhard Ganter	Bart Kosko	MacQueen, J.
Type	Lattice-based knowledge representation / concept mining	Fuzzy causal/feedback network for scenario analysis	Partitional clustering (centroid-based)
Source fondatrice	Wille, R. (1982). Restructuring lattice theory: an approach based on hierarchies of concepts. In I. Rival (Ed.), Ordered Sets (pp. 445–470). Reidel.	Kosko, B. (1986). Fuzzy cognitive maps. International Journal of Man-Machine Studies, 24(1), 65–75.	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	FCA, concept lattice analysis, Galois lattice, biçimsel kavram analizi	FCM, Kosko cognitive map, causal cognitive map, bulanık bilişsel haritalar	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering
Apparentées	3	4	3
Résumé	Formal concept analysis derives a hierarchy of concepts from a simple table of which objects have which attributes. Founded by Rudolf Wille in 1982 on lattice theory, it pairs each set of objects with the attributes they all share to form 'formal concepts', then organizes these into a concept lattice — a mathematically grounded, interpretable hierarchy used for knowledge discovery, ontology building, and explainable analysis of categorical data.	A fuzzy cognitive map, introduced by Bart Kosko in 1986, represents a system as a network of concepts connected by signed, weighted causal links, and simulates how the concepts influence one another over time. By combining the intuitive structure of a cognitive map with fuzzy weights and iterative activation, FCMs let experts encode causal knowledge and then run what-if scenarios — making them popular for policy analysis, strategic decision-making, and modelling complex socio-technical systems.	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.