Graph drawing by force-directed placement
Software—Practice & Experience
A discovery system for trigonometric functions
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Fab: content-based, collaborative recommendation
Communications of the ACM
Learning to extract symbolic knowledge from the World Wide Web
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Visualizing Semantic Clusters in the Internet Information Space
DS '98 Proceedings of the First International Conference on Discovery Science
Incorporating a Navigation Tool into a WWW Browser
DS '98 Proceedings of the First International Conference on Discovery Science
Machine discovery based on numerical data generated in computer experiments
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Discovery of Web Communities Based on the Co-Occurence of References
DS '00 Proceedings of the Third International Conference on Discovery Science
A Method for Discovering Purified Web Communities
DS '01 Proceedings of the 4th International Conference on Discovery Science
Evaluating the Jaccard-Tanimoto Index on Multi-core Architectures
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
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This paper describes a new method of discovering clusters of related Web pages. By clustering Web pages and visualizing them in the form of graph, users can easily access to related pages. Since related Web pages are often referred from the same Web page, the number of cooccurrence of references in a search engine is used for discovering relation among pages. Two URLs are given to a search engine as keywords, and the value of the number of pages searched from both URLs divided by the number of pages searched from either URL, which is called Jaccard coefficient, is calculated as the criteria for evaluating the relation between the two URLs. The value is used for deciding the length of an edge in a graph so that vertices of related pages will be located close to each other. Our system based on the method succeeds in discovering clusters of various genres, although the system does not interpret the contents of the pages. The method of calculating Jaccard coefficient is easily processed by computer systems, and it is suitable for the discovery from the data acquired through the internet.