Algorithms for clustering data
Algorithms for clustering data
Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
ParaSite: mining structural information on the Web
Selected papers from the sixth international conference on World Wide Web
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Efficient identification of Web communities
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
An open graph visualization system and its applications to software engineering
Software—Practice & Experience - Special issue on discrete algorithm engineering
Modern Information Retrieval
Lexical and semantic clustering by web links
Journal of the American Society for Information Science and Technology - Special issue: Webometrics
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We propose a framework to extract topic maps from a set of Web pages. We use the clustering method with the Web pages and extract the topic map prototypes. We introduced the following two points to the existing clustering method: The first is merging only the linked Web pages, thus extracting the underlying relationships between the topics. The second is introducing weighting based on similarity from the contents of the Web pages and relevance between topics of pages. The relevance is based on the types of links with directories in Web sites structure and the distance between the directories in which the pages are located. We generate the topic map prototypes from the results of the clustering. Finally, users complete the prototype by labeling the topics and associations and removing the unnecessary items. For this paper, at the first step, we mounted the proposed clustering method and extracted the prototype with the method.