An authorization policy management framework for dynamic medical data sharing
Contribuinte(s) |
[Unknown] |
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Data(s) |
01/01/2007
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Resumo |
In this paper, we propose a novel feature reduction approach to group words hierarchically into clusters which can then be used as new features for document classification. Initially, each word constitutes a cluster. We calculate the mutual confidence between any two different words. The pair of clusters containing the two words with the highest mutual confidence are combined into a new cluster. This process of merging is iterated until all the mutual confidences between the un-processed pair of words are smaller than a predefined threshold or only one cluster exists. In this way, a hierarchy of word clusters is obtained. The user can decide the clusters, from a certain level, to be used as new features for document classification. Experimental results have shown that our method can perform better than other methods.<br /> |
Identificador | |
Idioma(s) |
eng |
Publicador |
IEEE Computer Society |
Relação |
http://dro.deakin.edu.au/eserv/DU:30008106/abawajy-authorizationpolicy-2007.pdf http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4438447&isnumber=4438371 |
Direitos |
2007, IEEE |
Palavras-Chave | #feature extraction #merging #pattern clustering #text analysis |
Tipo |
Conference Paper |