Bayesian network structure optimization method and device based on frequent item mining

The invention relates to a Bayesian network structure optimization method based on frequent item mining. The method comprises the following steps: acquiring a data set; constructing an association rule set according to association rules reflecting the association degree between the random variables; after frequent item sets are extracted from the data set, learning a preset Bayesian network, and obtaining a maximum Bayesian network structure set corresponding to the maximum frequent item set; according to the association rule set and the maximum Bayesian network structure set, extracting a white list and a black list from the data set, and then constructing penalty terms; and according to the penalty terms and the BDeu scoring function, obtaining a scoring function fused with prior, and then carrying out loop search by using a hill-climbing search algorithm to obtain an optimal Bayesian network structure. By adopting the method, the efficiency and accuracy of Bayesian network structure learning can be obvious