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| Registros recuperados : 281 | |
| 20. |  | OLIVEIRA, S. R. de M.; ZAÏANE, O. R.; SAYGIN, Y. Secure association rule sharing. In: PACIFIC-ASIA CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 8., 2004, Sidney, Australia. Advances in knowledge discovery and data mining: proceedings. Berlin: Springer, 2004. p. 74-85. (Lecture notes in artificial intelligence, 3056). Editores: Honghua Dai, Ramakrishnan Srikant, Chengqi Zhang. PAKDD 2004. Na publicação: Stanley R. M. Oliveira.| Biblioteca(s): Embrapa Agricultura Digital. |
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| Registros recuperados : 281 | |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
Registro Completo
|
Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
13/04/2005 |
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Data da última atualização: |
17/01/2020 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
OLIVEIRA, S. R. de M.; ZAÏANE, O. |
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Afiliação: |
STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; OSMAR R. ZAÏANE, University of Alberta. |
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Título: |
Privacy-preserving clustering by object similarity-based representation and dimensionality reduction transformation. |
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Ano de publicação: |
2004 |
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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. |
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Idioma: |
Inglês |
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Notas: |
Na publicação: Stanley R. M. Oliveira. |
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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. |
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Palavras-Chave: |
Pattern recognition; Privacidade; Reconhecimento de padrões. |
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Thesaurus NAL: |
Cluster analysis. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 01560nam a2200181 a 4500
001 1009007
005 2020-01-17
008 2004 bl uuuu u00u1 u #d
100 1 $aOLIVEIRA, S. R. de M.
245 $aPrivacy-preserving clustering by object similarity-based representation and dimensionality reduction transformation.$h[electronic resource]
260 $aIn: WORKSHOP ON PRIVACY AND SECURITY ASPECTS OF DATA MINING; IEEE INTERNATIONAL CONFERENCE ON DATA MINING, 4., 2004, Brighton, UK. Proceedings... Brighton: IEEE Computer Society, 2004. p. 21-30.$c2004
500 $aNa publicação: Stanley R. M. Oliveira.
520 $aPreserving 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.
650 $aCluster analysis
653 $aPattern recognition
653 $aPrivacidade
653 $aReconhecimento de padrões
700 1 $aZAÏANE, O.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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