%0 Journal Article
%A Nyima Tashi
%A QI Minhui
%A WANG Lin
%A YU Yongbin
%T Association rule mining algorithm for Hopfield neural network based on threshold adaptive memristor
%D 2019
%R 10.11772/j.issn.1001-9081.2018071497
%J Journal of Computer Applications
%P 728-733
%V 39
%N 3
%X Aiming at the inaccurate mining results of the Maximum Frequent Itemset mining algorithm based on Hopfield Neural Network (HNNMFI), an improved association rule mining algorithm for Hopfield neural network based on current ThrEshold Adaptive Memristor (TEAM) model was proposed. Firstly, TEAM model was used to design and implement synapses whose weights were set and updated by the ability of that threshold memristor continuously changes memristance value with square-wave voltage, and the input of association rule mining algorithm was self-adapted by the neural network. Secondly, the energy function was improved to align with standard energy function, and the memristance values were used to represent the weights, then the weights and bias were amplified. Finally, an algorithm of generating association rules from the maximum frequent itemsets was designed. A total of 1000 simulation experiments using 10 random transaction sets with size less than 30 were performed. Experimental results show that compared with HNNMFI algorithm, the proposed algorithm improves the result accuracy of association mining by more than 33.9%, which indicates that the memristor can effectively improve the result accuracy of Hopfield neural network in association rule mining.
%U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2018071497