Porovnať metódy

	Granulované výpočty (informácie v granuloch)	Fuzzy kognitívne mapy (FCM)	K-Means Clustering
Odbor	Soft computing	Soft computing	Strojové učenie
Rodina	Machine learning	Process / pipeline	Machine learning
Rok vzniku	1997	1986	1967
Tvorca	Lotfi A. Zadeh (information granulation); developed by Pedrycz, Skowron, Yao	Bart Kosko	MacQueen, J.
Typ	Framework for multi-granularity information processing	Fuzzy causal/feedback network for scenario analysis	Partitional clustering (centroid-based)
Pôvodný zdroj	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.	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.
Ďalšie názvy	information granulation, computing with granules, three-way granular computing, tanecikli hesaplama	FCM, Kosko cognitive map, causal cognitive map, bulanık bilişsel haritalar	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering
Príbuzné	3	4	3
Zhrnutie	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.	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.