| 摘要: |
| 随着互联网信息的增长,Web挖掘已经成为数据挖掘研究的热点之一.网页分类是通过学习大量的带有类别标注的训练样本来预测网页的类别,人工标注这些训练样本是相当繁琐的.网页聚类通过一定的相似性度量,将相关网页归并到一类.然而传统的聚类算法对解空间的搜索带有盲目性和缺乏语义特征.提出了两阶段的半监督文本学习策略.第1阶段,利用贝叶斯潜在语义模型来标注含有潜在类别主题词变量的网页的类别;第2阶段,利用简单贝叶斯模型,在第1阶段类别标注的基础上,通过EM(expectation maximization)算法对不含有潜在类别主题词变量的文档作类别标注.实验结果表明,该算法具有很高的精度和召回率. |
| 关键词: 贝叶斯潜在语义分析 半监督学习 简单贝叶斯分类 期望最大化算法 Web挖掘 |
| DOI： |
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| 基金项目:国家自然科学基金资助项目(60073019,69803010) |
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| Semi-Supervised Web Mining Based on Bayes Latent Semantic Model |
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GONG Xiu-jun,SHI Zhong-zhi
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| Abstract: |
| With the increasing of information on Internet, Web mining has been the focus of data mining. Web classification predicts the labels of Web documents by learning lots of training examples with labels. It is very expensive to get these examples by manual. Web clustering groups the similar Web documents by a certain of metric of similarity. But the classical algorithms of clustering are aimless in searching the solution space and absent of semantic characters. In this paper, a semi-supervised learning strategy consists of tow stages is put forward.The fist atage,labels the documents the documents that include latent class variables by using Bayes latent semantic model.The second stage,based on the results from the first stage,labels the documents excluding latent class variables with the Naive Bayes models.Experimental results show that this algorithm has good precision and recall rate. |
| Key words: Bayes latent semantic analysis semi-supervised learning Naive Bayesian classifier expectation maximization Web mining |
