An efficient clustering framework for relevant web information
Ok‐Ran Jeong, Sang-Won Lee · 2009
As the amount of available information on the Internet grows, it is becoming increasingly difficult for users to find information that is relevant to their needs. Against this backdrop, a need for an automated tool that can find information quickly and easily has surfaced. In this paper, we propose a Clustering Framework for crawling and clustering the necessary information from Web pages. The proposed clustering framework consists of three modules: a preprocessing module, clustering module and community module. Using this framework, we are able to automatically cluster Web pages according to topic and rank them in terms of relevance. We describe this framework, and show the results of our preliminary validation work.