基于K-均值聚类的朴素贝叶斯网络分类模型

针对朴素贝叶斯网络分类模型在处理高维大数据量时的效率偏低和准确率有待提高的问题,结合主元分析法与K-均值聚类算法构造出了一个改进的朴素贝叶斯网络分类模型;摒弃了非类属性变量相对于类属性变量相对独立的前提条件,算法首先用主元分析法在对数据集的信息量尽量保存的同时进行了降维操作,使得算法可以着重于进行分类问题;算法还提出了一个"相对融合点"的概念,有效地提高了算法的性能;最后对算法的性能进行了分析,并将改进的算法应用到实际的数据集进行实验,用算法产生的分类结果对数据集中产生的一些缺失数据进行修补。

A Naive Bayesian Network Classification Model Based on K-means Clustering

According to the low efficiency and low accuracy of the naive Bayesian network classification model in dealing with large number of high-dimensionaldata,by combining Principal Component Analysis and K-means clustering algorithm,this paper gives an improved Naive Bayesian network calssification model.The model abandoned the premise for the relative independence between non-calss attribute variables and class attribute variables.Firstly,we use principal component analysis to reduce the dimensionality of the data set,so the algorithm can focus on the classification problem.The algotithm has also proposed aconcept called "relative fusion point" to effectively improve the performance of the algorithm.Finally,the performance of the algorithm is analyzed,and the improved algorithm is applied to the actual data set for experiment to repair the missing data of the data set,the results show that the algorithm is effective.