粗糙集和支持向量机相融合的Web数据挖掘

Web Data Mining Based on Rough Sets and Support Vector Machine

提出一种粗糙集和支持向量机相融合的Web数据挖掘模型.首先收集相关Web数据,提取特征,并采用粗糙集对特征进行约简,去除一些无用的特征,然后采用支持向量机对训练样本进行学习,建立Web数据挖掘模型,最后进行性能测试.实验结果表明,粗糙集和支持向量机相融合可以获得令人满意的Web数据挖掘效果,具有更高的实际价值.

In this paper a web data mining model was proposed,combined with rough sets and support vector machine （SVM）.Firstly,related web data should be collected,the characteristic must be extracted, and rough sets are used to reduce the characteristics.It is necessary to remove some useless features.Then, support vector machine is used to study the training sample.Web data mining model is established.At last, performance testing is needed.The experimental results show that the effect of web data mining is satisfac-tory combined with rough sets and support vector machine （SVM）.It has a higher practical value.