Comparar métodos

	Computação Granular (Granulação de Informação)	Agrupamento Hierárquico
Área	Soft computing	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	1997	1963
Autor original	Lotfi A. Zadeh (information granulation); developed by Pedrycz, Skowron, Yao	Ward, J. H.
Tipo	Framework for multi-granularity information processing	Unsupervised clustering (agglomerative)
Fonte seminal	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.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.
Outros nomes	information granulation, computing with granules, three-way granular computing, tanecikli hesaplama	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Relacionados	3	4
Resumo	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.	Hierarchical clustering is an unsupervised method that groups observations into nested clusters and draws the result as a dendrogram, so the number of clusters need not be fixed in advance. Its agglomerative form rests on the objective-function grouping criterion introduced by Joe Ward in 1963.