Título: Privacy Preserving Optimized Rules Mining from Decision Tables and Decision Trees
Autores: Alzahrani, Ahmed Saeed; Department of Computer Science, Faculty of Computing & I.T, King Abdulaziz University, Jeddah 21589
Qureshi, Muhammad Shuaib; Department of Computer Science, Faculty of Computing & I.T, King Abdulaziz University, Jeddah 21589
Fecha: 2012-06-01
Publicador: Indian journal of science and technology
Fuente:
Tipo: info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
Tema: Science; Technology
Genetic Algorithm, Decision Tree, Decision Table, Knowledge Base, Classification Rules
Descripción: With the swelling amount of data mining projects and information sharing, preserving privacy is a challenging issue which needs higher priority to guarantee security. This paper is an extension of Qureshi et al. (2010) with the addition of security features to the existing classification rules to preserve privacy. Experimental results show secure, accurate, sanitized and optimized rules which we achieve by using the concept of genetic algorithm (GA).
Idioma: Inglés

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