Enhancing Contents-Link Coupled Web Page Clustering and Its Evaluation

Web page clustering is a fundamental technique to offer a solution for data management, information locating and its interpretation of Web data and to facilitate users for navigation, discrimination and understanding. Most existing clustering algorithms cannot adapt well to Web clustering directly in terms of efficiency and effectiveness. Combining contents analysis and hyperlink structure analysis has been proven a better approach. However, how to effectively combine the two features with different nature in clustering to get satisfactory results remains an open problem and there is still little work on it. In this paper, we present an experimental study on enhancing coupling of links and contents analysis of Web pages for robust clustering. In particular, we introduce two techniques: in-link reinforcement and anchor window analysis to improve the adaptability of contents-link coupled clustering. Our detailed evaluation indicates those techniques can effectively improve the quality of Web pages clustering for a wide range of topics.