基于潜在语义差异的医学网页聚类  

Medical Webpage Clustering Based on Latent Semantic Difference

作　　者：米晓芳[1] 秦洋[1] 王立宏[1] 宋宜斌[1] 

机构地区：[1]烟台大学计算机学院,烟台264005

出　　处：《计算机工程》2008年第19期64-66,共3页Computer Engineering

基　　金：国家自然科学基金资助项目(60473115);山东省自然科学基金资助项目(Y2006G22)

摘　　要：采用潜在语义索引的全局模型和局部模型表示医学网页时,模糊聚类结果的类间包含度很大。该文提出一种新的潜在语义差异模型,将医学网页中的文本抽取出来并分别采用全局模型、局部模型和差异模型进行表示,利用FCM算法进行聚类并计算类间包含度。实验发现,对给定的5类医学网页进行聚类时,采用差异模型时的类间包含度平均约为全局模型的85%、局部模型的80%。Fuzzy clustering, two categories of medical Web pages represented by global LSI or local LSI generate two fuzzy sets with a large inclusion degree. A new latent semantic difference model is proposed. The text in medical Webpage is extracted and represented by global LSI, local LSI and difference LSI respectively. FCM algorithm is employed to cluster the feature vectors and inclusion degree between two result fuzzy sets is calculated. Experiments on five given categories of medical Webpages show that, on the average, difference LSI reduces the inclusion degree by a factor of 85% and 80% respectively when compared with global LSI and local LSI.

关 键 词：潜在语义索引 差异模型 文本挖掘 FCM聚类 包含度 

分 类 号：TP393.09[自动化与计算机技术—计算机应用技术]