One of the most frequent Web surfing tasks is to search for names of persons and organizations. Such names are often not distinctive, commonly occurring, and nonunique. Thus, a single name may be mapped to several entities. The paper describes a methodology to cluster the Web pages returned by the search engine so that pages belonging to different entities are clustered into different groups. The algorithm uses a combination of named entities, link-based and structure-based information as features to partition the document set into direct and indirect pages using a decision model. It then uses the distinct direct pages as seeds to cluster the document set into different clusters. The algorithm has been found to be effective for Web-based applications.