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.
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Pan, H., Tan, X., Han, Q., Feng, X., Yin, G. (2012). GMA: An Approach for Association Rules Mining on Medical Images. In: Huang, DS., Ma, J., Jo, KH., Gromiha, M.M. (eds) Intelligent Computing Theories and Applications. ICIC 2012. Lecture Notes in Computer Science(), vol 7390. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31576-3_54
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DOI: https://doi.org/10.1007/978-3-642-31576-3_54
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