Title :
A fast algorithm for mining association rules in medical image data
Author :
Olukunle, Adepele ; Ehikioya, Sylvanus
Author_Institution :
Dept. of Comput. Sci., Manitoba Univ., Winnipeg, Man., Canada
Abstract :
This paper presents a fast association rule mining algorithm which is suitable for medical image data sets. In particular, it assesses the feasibility of using association rule algorithms to extract hidden information from medical image data sets. In addition, we provide a flavour of our implementation environment. Finally, we show, with an example, how our proposed algorithm works to assess its suitability.
Keywords :
data mining; data structures; feature extraction; image texture; medical expert systems; medical image processing; visual databases; compact data representation scheme; data mining; fast association rule mining algorithm; feature extraction; frequent-pattern growth algorithm; hidden information; implementation environment; knowledge extraction; medical image data sets; rule generation; Association rules; Biomedical imaging; Computed tomography; Computer science; Data mining; Digital images; Magnetic resonance; Medical diagnostic imaging; Physics computing; Transaction databases;
Conference_Titel :
Electrical and Computer Engineering, 2002. IEEE CCECE 2002. Canadian Conference on
Print_ISBN :
0-7803-7514-9
DOI :
10.1109/CCECE.2002.1013116
