节点文献
一种基于关键特征的搜索引擎结果聚类算法
Key-feature-based clustering algorithm for search engine results
【摘要】 为了解决用户在搜索引擎结果列表中寻找所需信息困难的问题,帮助用户快速有效地定位有价值的Web文档,与向量空间模型方法不同,采用基于关键特征的聚类算法(KFC).首先从搜索引擎返回结果的关键词里选择重要的词作为关键特征,然后通过分析特征间的关系对特征聚类,最后基于特征聚类结果实现文档的聚类.通过对实验结果的测试表明了算法的有效性.
【Abstract】 To solve the problem that users of web search engines are often forced to sift through the long ordered list of document,a new key-feature clustering(KFC) algorithm was presented to help locate the valuable search results that the users really needed,which was different from VSM.The algorithm firstly extracted some key features from the
【关键词】 搜索引擎；
算法；
特征提取；
文档聚类；
向量空间模型VSM；
KFC算法；
【Key words】 in the search results. Then the relationships between key features were analyzed and features were clustered.Finally,the documents were clustered based on these clusters of key features.The algorithm was tested and validated by the results of experiments.Key words: search engines； algorithm； feature extraction； document clustering； vector space model； KFC algorithm；
【Key words】 in the search results. Then the relationships between key features were analyzed and features were clustered.Finally,the documents were clustered based on these clusters of key features.The algorithm was tested and validated by the results of experiments.Key words: search engines； algorithm； feature extraction； document clustering； vector space model； KFC algorithm；
【基金】 国家科技基础条件平台建设资助项目(2005DKA63901)
- 【文献出处】 北京航空航天大学学报 ,Journal of Beijing University of Aeronautics and Astronautics , 编辑部邮箱 ,2007年06期
- 【分类号】TP391.3
- 【被引频次】14
- 【下载频次】375