A Classifying Web Page Templates Model Based on Fuzzy K-Means Clustering Method
Huey-Ming Lee, Ching-Hao Mao, Yao-Jen Shih, Pin-Jen Chen, Mu-Hsiu Hsu, Jin-Shieh Su · 2006
Thousands of web pages rapidly expand every day, and the diversifications of web templates make us difficult to extract the contents of web pages. In this study, we proposed a classifying web page templates model based on fuzzy k-means clustering method. This model can automatically collect the web pages, generate several kinds of web pages templates, provide the different kinds of web content (e.g. hyperlink, image, text) templates for users' requests. Via the proposed model, we can not only classify the web pages templates more easily and efficiently, but also extract the appropriate web information on demands conveniently.