[ACM Press the ninth ACM SIGKDD international conference - Washington, D.C. (2003.08.24-2003.08.27)] Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '03 - Eliminating noisy information in Web pages for data mining

✍ Scribed by Yi, Lan; Liu, Bing; Li, Xiaoli

• Book ID: 125490627
• Publisher: ACM Press
• Year: 2003
• Tongue: English
• Weight: 458 KB
• Category: Article
• ISBN-13: 9781581137378
• DOI: 10.1145/956750.956785

✦ Synopsis

A commercial Web page typically contains many information blocks. Apart from the main content blocks, it usually has such blocks as navigation panels, copyright and privacy notices, and advertisements (for business purposes and for easy user access). We call these blocks that are not the main content blocks of the page the noisy blocks. We show that the information contained in these noisy blocks can seriously harm Web data mining. Eliminating these noises is thus of great importance. In this paper, we propose a noise elimination technique based on the following observation: In a given Web site, noisy blocks usually share some common contents and presentation styles, while the main content blocks of the pages are often diverse in their actual contents and/or presentation styles. Based on this observation, we propose a tree structure, called Style Tree, to capture the common presentation styles and the actual contents of the pages in a given Web site. By sampling the pages of the site, a Style Tree can be built for the site, which we call the Site Style Tree (SST). We then introduce an information based measure to determine which parts of the SST represent noises and which parts represent the main contents of the site. The SST is employed to detect and eliminate noises in any Web page of the site by mapping this page to the SST. The proposed technique is evaluated with two data mining tasks, Web page clustering and classification. Experimental results show that our noise elimination technique is able to improve the mining results significantly.