科研成果详情

随着World Wide Web上数据量的日益庞大,现有的搜索引擎已经不能满足用户日益增长的需求.利用数据挖掘技术,提高搜索效率,实现了查询的用户化.首先提出了模糊概念图的模型来描述词语间的关系,然后在聚类过程中引入概念知识,提出了基于模糊概念图的文档聚类算法,通过分析用户的浏览行为发现兴趣模式.在上述技术的基础上,给出了一种用户化的智能搜索系统的实现策略,通过分析概念间的关系和用户的兴趣模式,评价超链/文档和查询的相关程度,从而帮助用户得到更准确的信息. 

With the explosive growth of data available on World Wide Web, it seems that the current search engines cannot met the increasing requirement of users. This paper focuses on improving the effectiveness and the efficiency of Web search with data mining technology. A documental clustering algorithm is presented integrated with fuzzy concept graph for mining interest patterns. Based on the above technology, an intelligent customized search system is proposed that enables users to obtain useful information according to the relation of concepts and their own interests. The strategy is to evaluate the relevance of documents effectively based on fuzzy concept graph and user's personal interests.