基于关联规则的垃圾邮件分类模型

SPAM EMAIL CLASSIFICATION MODEL BASED ON ASSOCIATION RULES

为了提高垃圾邮件分类精确,提出一种基于关联规则的垃圾邮件分类模型。首先通过改进的FP-grow算法挖掘垃圾邮件关联规则集,以关联规则集为基础构建垃圾邮件分类器模型,然后考虑垃圾邮件特征词权重对邮件进行分类,最后采用仿真实验测试模型的性能。结果表明,该方法提高了垃圾邮件分类精度,可以较好地对垃圾邮件进行分类。

In order to improve spam email classification precision,we proposed a novel association rules-based spam email classification model. First,we used improved FP-grow algorithm to mine the association rules set of span emails,and built the spam email classifier model based on association rules set; then we classified the span emails by considering their feature words weights. Finally,we carried out simulation experiments to test the performance of the model. Results showed that the proposed model improved the classification precision on spam emails,and could better classify the spam emails.