Fuzzy Association Rule Mining Based on Rival Penalized Competitive Learning

It is very hard to know in advance the most appropriate fuzzy sets that are good enough for the domains of quantitative attributes for fuzzy association rules mining.So this paper proposes a fuzzy association rule mining algorithm based on Rival Penalized Competitive Learning(RPCL),which can get fuzzy sets and set the number of fuzzy sets without prior knowledge of the fuzzy linguistic terms but by quantitative attributes.Experimental results show that the algorithm can get more interesting association rules compared with other algorithms.