基于K-center和信息增益的Web搜索结果聚类方法  

Web search result clustering based on K-center and information gain

作　　者：丁振国[1] 孟星[1] 

机构地区：[1]西安电子科技大学计算机学院,西安710071

出　　处：《计算机应用研究》2008年第10期3125-3127,共3页Application Research of Computers

基　　金：国家"863"计划资助项目(2004AA1Z2520);军队网络互联与信息安全策略研究资助项目(2006QB1069)

摘　　要：基于K-center和信息增益的概念,将改进后的FPF(furthest-point-first)算法用于Web搜索结果聚类,提出了聚类标志方法,使得聚类呈现出的结果更易于用户理解,给出了评价聚类质量的模型。将该算法与Lingo,K-means算法进行比较,其结果表明,本算法能够较好地平衡聚类质量和速度,更加适用于Web检索聚类。Based on K-center and information gain, this paper represented a version of modified FPF algorithm and cluster labeling algorithm on Web search clustering, made the result better understood. At last, presented a simple and intuitionistic criterion NMI for estimating cluster quality. The proposed solution was evaluated in search results returned from actual Web search engine and compared with other methods, like Lingo, K-means. The result proves that the algorithm can balance better clustering time and quality, and meets the requirements of Web searching clustering.

关 键 词：WEB文档 聚类 聚类标志 K-center 信息增益 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]