Abstract
Medical Knowledge Sharing System can be extremely beneficial for people living in isolated communities and remote regions. Association rule is a very important Knowledge form. Finding these valuable rules from brain images is a significant research topic in the field of data mining. Discovering frequent itemsets is the key process in association rule mining. Traditional association rule algorithms adopt an iterative method which requires large amount of calculation. In this paper, we proposed a new algorithm which based on association graph and matrix (GMA) pruning to reduce the amount of candidate itemsets. Experimental results show that our algorithm is more efficient for different values of minimum support.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
