基于频繁项挖掘的贝叶斯网络结构学习算法BNSL-FIM

贝叶斯网络能够表示不确定知识并进行推理计算表达，但由于实际样本数据存在噪声和大小限制以及网络空间搜索的复杂性，贝叶斯网络结构学习始终会存在一定的误差。为了提高贝叶斯网络结构学习的准确度，提出了以最大频繁项集和关联规则分析结果为先验知识的贝叶斯网络结构学习算法BNSL-FIM 。首先从数据中挖掘出最大频繁项集并对该项集进行结构学习，之后使用关联规则分析结果对其进行校正，从而确定基于频繁项挖掘和关联规则分析的先验知识。然后提出一种融合先验知识的BDeu评分算法进行贝叶斯网络结构学习。最后在6个公开标准的数据集上开展了实验，并对比引入先验/不引入先验的结构与原始网络结构的汉明距离，结果表明所提算法与未引入先验的BDeu评分算法相比显著提高了贝叶斯网络结构学习的准确度。

BNSL-FIM:Bayesian network structure learning algorithm based on frequent item mining

Bayesian networks can represent uncertain knowledge and perform inferential computational expressions， but due to the noise and size limitations of actual sample data and the complexity of network space search， Bayesian network structure learning will always have certain errors. To improve the accuracy of Bayesian network structure learning， a Bayesian network structure learning algorithm with the results of maximum frequent itemset and association rule analysis as the prior knowledge was proposed， namely BNSL-FIM （Bayesian Network Structure Learning algorithm based on Frequent Item Mining）. Firstly， the maximum frequent itemset was mined from data and the structure learning was performed on the itemset， then the association rule analysis results were used to correct it， thereby determining the prior knowledge based on frequent item mining and association rule analysis. Secondly， a Bayesian Dirichlet equivalent uniform （BDeu） scoring algorithm was proposed combining with prior knowledge for Bayesian network structure learning. Finally， experiments were carried out on 6 public standard datasets to compare the Hamming distance between the structure with/without prior and the original network structure. The results show that the proposed algorithm can effectively improve the structure learning accuracy of Bayesian network compared to the original BDue scoring algorithm.