Data/hora: 16/06/2024 06:04:06

Biblioteca(s): 	Embrapa Agricultura Digital.	
Data corrente: 	26/08/1996	
Data da última atualização: 	10/01/2008	
Autoria: 	FISHER, D.; LANGLEY, P.	
Título: 	Conceptual clustering and its relation to numerical taxonomy.	
Ano de publicação: 	1986	
Fonte/Imprenta: 	In: WORKSHOP ON ARTIFICIAL INTELLIGENCE AND STATISTICS, 1985, Princeton. Artificial intelligence and statistics. Reading: Addison-Wesley, 1986.	
Páginas: 	p.77-116.	
Idioma: 	Inglês	
Notas: 	Edited by Willian A. Gale.	
Conteúdo: 	Artificial Intelligence (AI) methods for machine learning can be viewed as forms of exploratory data analysis, even though they differ markedly from the statitical methods generally commoted by the term. The distinction between methods of machine learning and statistical data analysis is primarily due to differences in the way techniques of each type represent data and structure within data. That is, methods of machine learning are strongly biased toward symbolic (as opposed to numeric) data representations. We explore this difference within a limited context, devoting the bulk of our chapter to the explication of conceptual clustering, an extension to the statiscally based methods of numerical taxonomy. In conceptual clustering the formation of object clusters is dependent on the quality of 'higher level' characterizations, termed concepts, of the clusters. The form of concepts used by existing conceptual clustering systems (sets of necessary and sufficient conditions) is described in some detail. This is followed by descriptions of several conceptual clustering techniques, along with sample output. We conclude with a discussion of how alternative concept reprsentations might enance the effectiveness of future conceptual clustering systems.	
Categoria do assunto: 	--	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
Exemplares: 	CNPTIA - (3937-1) - ADD - PL - -- - 006.3 - WOR - 1986.00021 - DIS