Web multimedia information retrieval using improved Bayesian algorithm

The main thrust of this paper is application of a novel data mining approach on the log of user's feedback to improve web multimedia information retrieval performance. A user space model was constructed based on data mining, and then integrated into the original information space model to improve the accuracy of the new information space model. It can remove clutter and irrelevant text information and help to eliminate mismatch between the page author's expression and the user's understanding and expectation. User space model was also utilized to discover the relationship between high-level and low-level features for assigning weight. The authors proposed improved Bayesian algorithm for data mining. Experiment proved that the authors' proposed algorithm was efficient. Project (No. 20020335020) supported by Research Fund for Doctoral Program, Ministry of Education of China     

一种改进的Apriori算法在web日志挖掘中的应用

在对web日志挖掘的处理流程以及难点深入分析的基础上,为了达到更快挖掘频繁访问页面组的目标,提出一种改进的Apriori算法,主要通过减少候选项集和对事务数据库的压缩来实现性能的提高.候选项集的减少是通过对频繁项集的缩减间接来实现,事务数据库的压缩则通过一系列预先定义的规则来实现.实验数据表明,无论对于短事务集,还是长事务集,算法的性能都得到了提升,更好地满足了实际应用的需要.     

Web日志挖掘在校园网建设中的应用研究

在Web日志挖掘的基础上,构建挖掘系统模型,采用模糊聚类方法对采集的日志数据进行聚类,得到用户的访问模式,从而指导校园网网站管理人员改善Web站点结构,提高用户查找信息的准确率和效率。