基于K-means的朴素贝叶斯分类算法的研究

将K-means算法引入到朴素贝叶斯分类研究中,提出一种基于K-means的朴素贝叶斯分类算法。首先用K-means算法对原始数据集中的完整数据子集进行聚类,计算缺失数据子集中的每条记录与k个簇重心之间的相似度,把记录赋给距离最近的一个簇,并用该簇相应的属性均值来填充记录的缺失值,然后用朴素贝叶斯分类算法对处理后的数据集进行分类。实验结果表明,与朴素贝叶斯相比,基于K-means思想的朴素贝叶斯算法具有较高的分类准确率。

Research of Naive Bayesian Classification Based on K-means Method

A naive Bayesian classification based on K-means method by introducing clustering algorithm into naive Bayesian classification.The similarity between every recorder in absent data subsets and the centers k cluster is calculated by clustering complete data subsets of initial data by K-means algorithm,then the recorder is set to the nearest cluster and the absent value of the record is filled by the corresponding attribute of the cluster,finally,the handled data set is clustered by naive Bayesian classification algorithm.The experiments show that naive Bayesian classification based on K-means method has the higher precise of clustering comparing with naive Bayesian classification.