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- W2363803847 abstract "Objective:The purpose of this study was to mine the correlative relationship between massive amounts of medical image data. Methods: Currently most researchers concern about the investigation of support degree, but neglect the confidence degree. This paper associated two aspects, support degree and confidence degree that two measure standard of interestingness, and PC-tree frequent pattern correlation rules algorithm with high time-space efficiency, to analysis medical image data. Results: The experiment showed that this method can gain more efficient associative and correlative rules while eliminate the negative correlation rules. Conclusions: This method can provide more important value for medical diagnose." @default.
- W2363803847 created "2016-06-24" @default.
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- W2363803847 date "2009-01-01" @default.
- W2363803847 modified "2023-09-28" @default.
- W2363803847 title "Associative and Correlative Regular Mining Based on Interestingness and PC-tree for Medical Images Data" @default.
- W2363803847 hasPublicationYear "2009" @default.
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