A Method of Automatic Web Information Extraction Based on Page Clustering

Dynamic web page has a large amount of pages, high-value data and high-modularity structure. According to these feature, this paper developed an automatic web information extraction system based on page clustering. On the basis of DOM extraction technique, it used page clustering to find the high similarity clusters, and improved the accuracy of clustering results by using the column similarity measure and global auto-similarity measure. Extraction template applied the optional nodes to modify and adjust the template in order to improve the identification of the content nodes. Experimental result shows this method automatically locates and extracts the main information of pages and achieves high precision and recall.