Medical Webpage Clustering Based on Latent Semantic Difference
MI Xiao-fang, Yang Qin, Lihong Wang, Yibin Song · Jisuanji gongcheng · 2008
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.