Data/hora: 06/06/2024 14:25:38

Biblioteca(s): 	Embrapa Agricultura Digital.	
Data corrente: 	13/04/2005	
Data da última atualização: 	17/01/2020	
Autoria: 	OLIVEIRA, S. R. de M.; ZAÏANE, O.	
Afiliação: 	STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; OSMAR R. ZAÏANE, University of Alberta.	
Título: 	Privacy-preserving clustering by object similarity-based representation and dimensionality reduction transformation.	
Ano de publicação: 	2004	
Fonte/Imprenta: 	In: WORKSHOP ON PRIVACY AND SECURITY ASPECTS OF DATA MINING; IEEE INTERNATIONAL CONFERENCE ON DATA MINING, 4., 2004, Brighton, UK. Proceedings... Brighton: IEEE Computer Society, 2004. p. 21-30.	
Idioma: 	Inglês	
Notas: 	Na publicação: Stanley R. M. Oliveira.	
Conteúdo: 	Preserving privacy of individuals when data are shared for clustering is a challenging problem. Data owners must not only meet privacy requirements but also guarantee valid clustering results. In this paper, we show that this dual goal can be achieved by transforming a database using two simple and effective data transformations: Object Similarity-Based Representation (OSBR) and Dimensionality Reduction-Based Transformation (DRBT). The former relies on the idea behind the similarity between objects, and the latter relies on the intuition behind random projection. The major features of our data transformations are: a) they are independent of distance-based clustering algorithms; b) they have a sound mathematical foundation; and c) they do not require CPU-intensive operations.	
Palavras-Chave: 	Pattern recognition; Privacidade; Reconhecimento de padrões.	
Thesaurus Nal: 	Cluster analysis.	
Categoria do assunto: 	X Pesquisa, Tecnologia e Engenharia	
Registro original: 	Embrapa Agricultura Digital (CNPTIA)	
Exemplares: 	CNPTIA - (10662-1) - UPC - AA - -- -  -  - 2005.00005 - DES
CNPTIA - (10662-2) - UPC - AA - DD -  -  -  - DIS